Molecular Evolutionary Genetics Analysis

Molecular Evolutionary Genetics Analysis
MEGA
Molecular Evolutionary Genetics Analysis
Version 4.0 (Beta release)
Koichiro Tamura, Joel Dudley
Masatoshi Nei, Sudhir Kumar,
Draft
Center for Evolutionary Functional Genomics
Biodesign Institute
Arizona State University
Molecular Evolutionary Genetics Analysis
Table of Contents
1
2
Preface.................................................................................................................10
1.1
Preface .........................................................................................................10
1.2
Copyright .....................................................................................................11
1.3
Disclaimer....................................................................................................11
1.4
Acknowledgements.......................................................................................11
1.5
MEGA 4 Software Development Team ........................................................12
1.6
Citing MEGA in Publications ......................................................................12
Part I: Getting Started.......................................................................................14
2.1
Installing MEGA ..........................................................................................14
2.11 System Requirements...............................................................................14
2.12 Installing MEGA......................................................................................14
2.13 Uninstalling MEGA .................................................................................14
2.2
Features & Support......................................................................................15
2.21 What's New in Version 4 .........................................................................15
2.22 Feature List ..............................................................................................15
2.23 Using MEGA in the Classroom ...............................................................25
2.24 Technical Support and Updates ...............................................................25
2.25 Reporting Bugs ........................................................................................25
2.26 Guide to Notations Used..........................................................................26
2.3
A Walk Through MEGA ...............................................................................26
2.31 Creating Multiple Sequence Alignments .................................................27
2.32 Estimating Evolutionary Distances from Nucleotide Sequences.............28
2.33 Constructing Trees and Selecting OTUs from Nucleotide Sequences ....29
2.34 Tests of the Reliability of a Tree Obtained..............................................32
2.35 Working With Genes and Domains .........................................................33
2.36 Test of Positive Selection.........................................................................34
2.37 Managing Taxa With Groups...................................................................35
2.38 Computing Statistical Quantities for Nucleotide Sequences ...................35
2.39 Constructing Trees from Distance Data...................................................37
3
Part II: Assembling Data for Analysis .............................................................39
3.1
Trace Data File Viewer/Editor ....................................................................41
3.2
Web Browser................................................................................................42
3.3
Some Text Editor Utilities............................................................................43
3.31 Open Saved Alignment Session...............................................................43
3.32 Copy Screenshot to Clipboard .................................................................43
3.33 Format Selected Sequence .......................................................................44
3.34 Reverse Complement ...............................................................................44
3.35 Convert to Mega Format (in Text Editor)................................................44
3
Preface
3.4
Building Sequence Alignments.....................................................................45
3.41 Alignment Explorer .................................................................................45
3.42 Aligning coding sequences via protein sequences...................................45
3.43 CLUSTALW............................................................................................46
3.44 BLAST.....................................................................................................49
3.45 Menu Items in the Alignment Explorer ...................................................49
4
Part III: Input Data Types and File Format ..................................................56
4.1
MEGA Input Data Formats .........................................................................56
4.11 MEGA Format .........................................................................................56
4.12 General Conventions................................................................................56
4.13 Sequence Input Data ................................................................................58
4.14 Site Label .................................................................................................63
4.15 Labeled Sites............................................................................................64
4.16 Distance Input Data..................................................................................65
4.17 Tree Input Data ........................................................................................66
4.2
Importing Data from other Formats ............................................................67
4.21 Importing Data From Other Formats .......................................................67
4.22 Convert To MEGA Format (Main File Menu) ........................................68
4.23 Format Specific Notes..............................................................................68
4.3
Genetic Code Tables ....................................................................................84
4.31 Built-in Genetic Codes.............................................................................85
4.32 Adding/Modifying Genetic Code Tables.................................................86
4.33 Computing Statistical Attributes (Genetic Code) ....................................86
4.34 Code Table Editor ....................................................................................88
4.4
Viewing and Exploring Input Data ..............................................................88
4.41 Sequence Data Explorer...........................................................................88
4.42 Sequence Data Explorer...........................................................................88
4.43 Distance Data Explorer ............................................................................99
4.44 Text Editor .............................................................................................102
4.5
Visual Tools for Data Management...........................................................105
4.51 Setup/Select Genes & Domains .............................................................105
4.52 Groups of taxa........................................................................................106
4.53 Data Subset Selection ............................................................................106
5
Part IV: Evolutionary Analysis .....................................................................107
5.1
Computing Basic Statistical Quantities for Sequence Data ......................107
5.11 Basic Sequence Statistics.......................................................................107
5.12 Nucleotide and Amino Acid Compositions ...........................................107
5.2
Computing Evolutionary Distances ...........................................................107
5.21 Distance Models.....................................................................................107
5.22 Specifying Distance Estimation Options ...............................................147
5.23 Compute Pariwise ..................................................................................149
5.24 Compute Means .....................................................................................149
5.25 Compute Sequence Diversity.................................................................149
5.3
Constructing Phylogenetic Trees...............................................................149
5.31 Phylogenetic Inference...........................................................................149
4
Molecular Evolutionary Genetics Analysis
5.32
5.33
5.34
5.35
5.36
5.37
5.38
5.39
NJ/UPGMA Methods.............................................................................150
Minimum Evolution Method .................................................................151
Maximum Parsimony (MP) Method......................................................153
Branch-and-Bound algorithm ................................................................153
Min-mini algorithm................................................................................155
Maximum Composite Likelihood Method.............................................155
Statistical Tests of a Tree Obtained .......................................................156
Handling Missing Data and Alignment Gaps ........................................158
5.4
Tests of Selection........................................................................................159
5.41 Synonymous/Nonsynonymous Tests .....................................................160
5.42 Other Tests .............................................................................................164
5.5
Molecular Clock Test.................................................................................164
5.6
Substitution Pattern ...................................................................................165
5.61 Pattern Menu..........................................................................................165
5.62 Compute Pattern Disparity Index...........................................................165
5.63 Compute Composition Distance ............................................................165
5.64 Compute Transition/Transversion Bias .................................................166
5.65 Pattern | Compute Transition/Transversion Bias ® ...............................166
6
Part V: Visualizing and Exploring Data and Results ...................................167
6.1
Distance Matrix Explorer ..........................................................................167
6.11 Distance Matrix Explorer.......................................................................167
6.12 Average Menu (in Distance Matrix Explorer) .......................................168
6.13 Display Menu (in Distance Matrix Explorer) ........................................168
6.14 File Menu (in Distance Matrix Explorer) ..............................................169
6.2
Sequence Data Explorer ............................................................................169
6.21 Data Menu..............................................................................................169
6.22 Display Menu.........................................................................................169
6.23 Highlight Menu......................................................................................169
6.24 Statistics Menu.......................................................................................169
6.3
Tree Explorer .............................................................................................169
6.31 Tree Explorer .........................................................................................169
6.32 Information Box.....................................................................................170
6.33 File Menu (in Tree Explorer).................................................................170
6.34 Image Menu (in Tree Explorer) .............................................................171
6.35 Subtree Menu (in Tree Explorer)...........................................................171
6.36 Subtree Drawing Options (in Tree Explorer).........................................172
6.37 Cutoff Values Tab..................................................................................173
6.38 Divergence Time Dialog Box ................................................................173
6.39 View Menu (in Tree Explorer) ..............................................................173
6.310
Options dialog box (in Tree Explorer)...............................................173
6.311
Tree tab (in Options dialog box)........................................................173
6.312
Branch tab (in Options dialog box)....................................................174
6.313
Labels tab (in Options dialog box).....................................................174
6.314
Scale Bar tab (in Options dialog box)................................................174
6.315
Compute Menu (in Tree Explorer) ....................................................174
6.4
Caption Expert ...........................................................................................175
5
Preface
6.41
7
Creating Data Captions with Caption Expert.........................................175
Appendix...........................................................................................................176
7.1
Frequently Asked Questions ......................................................................176
7.11 Computing statistics on only highlighted sites in Data Explorer...........176
7.12 Finding the number of sites in pairwise comparisons............................176
7.13 Get more information about the codon based Z-test for selection.........176
7.14 Menus in MEGA are so short; where are all the options? .....................176
7.15 Writing only 4-fold degenerate sites to an output file ...........................177
7.2
Main Menu Items and Dialogs Reference..................................................177
7.21 Main MEGA Menus ..............................................................................177
7.22 MEGA Dialogs ......................................................................................189
7.3
Error Messages..........................................................................................194
7.31 Blank Names Are Not Permitted ...........................................................194
7.32 Data File Parsing Error ..........................................................................194
7.33 Dayhoff/JTT Distance Could Not Be Computed...................................194
7.34 Domains Cannot Overlap.......................................................................194
7.35 Equal Input Correction Failed................................................................194
7.36 Fisher's Exact Test Has Failed ...............................................................194
7.37 Gamma Distance Failed Because p > 0.99 ............................................194
7.38 Gene Names Must Be Unique................................................................195
7.39 Inapplicable Computation Requested ....................................................195
7.310
Incorrect Command Used ..................................................................195
7.311
Invalid special symbol in molecular sequences .................................195
7.312
Jukes-Cantor Distance Failed ............................................................195
7.313
Kimura Distance Failed .....................................................................195
7.314
LogDet Distance Could Not Be Computed .......................................195
7.315
Missing data or invalid distances in the matrix .................................196
7.316
No Common Sites ..............................................................................196
7.317
Not Enough Groups Selected.............................................................196
7.318
Not Enough Taxa Selected.................................................................196
7.319
Not Yet Implemented.........................................................................196
7.320
p distance is found to be > 1 ..............................................................196
7.321
Poisson Correction Failed because p > 0.99 ......................................197
7.322
Tajima-Nei Distance Could Not Be Computed .................................197
7.323
Tamura (1992) Distance Could Not Be Computed............................197
7.324
Tamura-Nei Distance Could Not Be Computed ................................197
7.325
Unexpected Error ...............................................................................197
7.326
User Stopped Computation ................................................................197
7.4
Glossary .....................................................................................................197
7.41 ABI File Format.....................................................................................197
7.42 Alignment Gaps .....................................................................................198
7.43 Alignment session..................................................................................198
7.44 Bifurcating Tree .....................................................................................198
7.45 Branch ....................................................................................................198
7.46 ClustalW ................................................................................................198
7.47 Codon.....................................................................................................198
7.48 Codon Usage..........................................................................................198
6
Molecular Evolutionary Genetics Analysis
7.49 Complete-Deletion Option.....................................................................198
7.410
Composition Distance........................................................................199
7.411
Compress/Uncompress ......................................................................199
7.412
Condensed Tree .................................................................................199
7.413
Constant Site ......................................................................................199
7.414
Degeneracy ........................................................................................199
7.415
Disparity Index...................................................................................199
7.416
Domains .............................................................................................200
7.417
Exon ...................................................................................................200
7.418
Extant Taxa ........................................................................................200
7.419
Flip .....................................................................................................200
7.420
Format command ...............................................................................200
7.421
Gamma parameter..............................................................................200
7.422
Gene ...................................................................................................200
7.423
Genetic Codes ....................................................................................201
7.424
Indels..................................................................................................201
7.425
Independent Sites ...............................................................................201
7.426
Inferred Tree ......................................................................................201
7.427
Intron..................................................................................................201
7.428
Maximum Composite Likelihood ......................................................201
7.429
Max-mini branch-and-bound search ..................................................201
7.430
Maximum Parsimony Principle .........................................................201
7.431
Mid-point rooting...............................................................................202
7.432
Monophyletic .....................................................................................202
7.433
mRNA ................................................................................................202
7.434
NCBI ..................................................................................................202
7.435
Newick Format...................................................................................202
7.436
Node...................................................................................................203
7.437
Nonsynonymous change ....................................................................203
7.438
Nucleotide Pair Frequencies ..............................................................203
7.439
OLS branch length estimates .............................................................203
7.440
Orthologous Genes.............................................................................204
7.441
Outgroup ............................................................................................204
7.442
Pairwise-deletion option ....................................................................204
7.443
Parsimony-informative site................................................................204
7.444
Polypeptide ........................................................................................204
7.445
Positive selection ...............................................................................204
7.446
Protein parsimony ..............................................................................204
7.447
Purifying selection .............................................................................204
7.448
Purines................................................................................................205
7.449
Pyrimidines ........................................................................................205
7.450
Random addition trees .......................................................................205
7.451
Rooted Tree........................................................................................205
7.452
RSCU .................................................................................................205
7.453
Singleton Sites ...................................................................................205
7.454
Staden.................................................................................................205
7.455
Statements in input files.....................................................................206
7.456
Swap...................................................................................................206
7.457
Synonymous change ..........................................................................206
7.458
Taxa....................................................................................................206
7
Preface
7.459
7.460
7.461
7.462
7.463
7.464
7.465
7.466
7.467
7.468
7.5
Topological distance ..........................................................................206
Topology ............................................................................................206
Transition ...........................................................................................207
Transition Matrix ...............................................................................207
Transition/Transversion Ratio (R) .....................................................207
Translation .........................................................................................207
Transversion.......................................................................................207
Tree length .........................................................................................207
Unrooted tree .....................................................................................208
Variable site .......................................................................................208
Reference....................................................................................................208
8
Molecular Evolutionary Genetics Analysis
9
Preface
1 Preface
1.1 Preface
Genome sequencing is generating vast amounts of DNA sequence data from a wide
range of organisms. As a result, gene sequence databases are growing rapidly. In
order to conduct efficient analyses of these data, there is a need for easy-to-use
computer programs, containing fast computational algorithms and useful statistical
methods.
The objective of the MEGA software has been to provide tools for exploring,
discovering, and analyzing DNA and protein sequences from an evolutionary
perspective. The first version was developed for the limited computational
resources that were available on the average personal computer in early 1990s.
MEGA1 made many methods of evolutionary analysis easily accessible to the
scientific community for research and education. MEGA2 was designed to harness
the exponentially greater computing power and graphical interfaces of the late
1990’s, fulfilling the fast-growing need for more extensive biological sequence
analysis and exploration software. It expanded the scope of its predecessor from
single gene to genome wide analyses. Two versions were developed (2.0 and 2.1),
each supporting the analyses of molecular sequence (DNA and protein sequences)
and pairwise distance data. Both could specify domains and genes for multi-gene
comparative sequence analysis and could create groups of sequences that would
facilitate the estimation of within- and among- group diversities and infer the
higher-level evolutionary relationships of genes and species. MEGA2 implemented
many methods for the estimation of evolutionary distances, the calculation of
molecular sequence and genetic diversities within and among groups, and the
inference of phylogenetic trees under minimum evolution and maximum
parsimony criteria. It included the bootstrap and the confidence probability tests of
reliability of the inferred phylogenies, and the disparity index test for examining
the heterogeneity of substitution pattern between lineages.
MEGA 4 continues where MEGA2 left off, emphasizing the integration of sequence
acquisition with evolutionary analysis. It contains an array of input data and
multiple results explorers for visual representation; the handling and editing of
sequence data, sequence alignments, inferred phylogenetic trees; and estimated
evolutionary distances. The results explorers allow users to browse, edit,
summarize, export, and generate publication-quality captions for their results.
MEGA 4 also includes distance matrix and phylogeny explorers as well as
advanced graphical modules for the visual representation of input data and output
results. These features, which we discuss below, set MEGA apart from other
comparative sequence analysis programs
As with previous versions, MEGA 4 is specifically designed to reduce the time
needed for mundane tasks in data analysis and to provide statistical methods of
molecular evolutionary genetic analysis in an easy-to-use computing workbench.
While MEGA 4 is distinct from previous versions, we have made a special effort to
retain the user-friendly interface that researchers have come to identify with
10
Molecular Evolutionary Genetics Analysis
MEGA. This interface is obtains information from the user only on a need-to-know
basis. Furthermore, the data subsets and output results are stored in files for
viewing only if the user specifically needs to do so.
1.2 Copyright
Copyright © 1993, 1994, 2000, 2001, 2004, 2005, 2006.
This software is protected under the copyright law. No part of this manual or
program design may be reproduced without written permission from copyright
holders. Please e-mail all inquires to [email protected]
1.3 Disclaimer
Although the utmost care has been taken to ensure the correctness of the software,
it is provided "as is," without any warranty of any kind. In no event shall the
authors or their employers be considered liable for any damages, including, but not
limited to, special, consequential, or other damages. The authors specifically
disclaim all other warranties, expressed or implied, including, but not limited to,
the determination of the suitability of this product for a specific purpose, use or
application.
Note that brand and product names (e.g., Windows and Delphi) are trademarks or
registered trademarks of their respective holders.
1.4 Acknowledgements
Many friends and colleagues have provided encouragement and assistance in the
development of MEGA. Beta Test versions of MEGA have been used in the
research laboratories of the authors, in the classrooms of Sudhir Kumar at the
Arizona State University and Masatoshi Nei at the Pennsylvania State University,
and by the thousands of users that signed up for the MEGA 4 Beta program. The
feedback and bug reports provided by these groups of users were invaluable to the
development team. Almost all facets of design and implementation benefited from
their comments and suggestions.
MEGA software development is supported by research grants from the NIH, NSF,
and Burroughs-Wellcome Fund.
11
Preface
1.5 MEGA 4 Software Development Team
Project Director
Sudhir Kumar
Programming Efforts
Principal Programmers
Koichiro Tamura & Sudhir Kumar
Associate Programmer
Joel Dudley
User Interface Design
Sudhir Kumar & Koichiro Tamura
Documentation
Sudhir Kumar
Koichiro Tamura
Joel Dudley
Website Designs and Implementation
Wayne Parkhurst
Joel Dudley
Quality Assurance
Graziela Valente
See also Acknowledgements.
1.6 Citing MEGA in Publications
WARNING: This version of MEGA is for testing purposes only. Please do not publish
results obtained with this version.
If you wish to cite MEGA in your publications, we suggest the following:
(1) When referring to MEGA in the main text of your publication, you may choose a
format such as:
Phylogenetic and molecular evolutionary analyses were conducted using MEGA
version 4 (Tamura, Dudley, Nei, and Kumar 2006).
12
Molecular Evolutionary Genetics Analysis
(For later versions of MEGA, replace 4 with the appropriate version number [e.g., 4.5],
which is displayed at the top of the main MEGA application window.
(2) When including a MEGA citation in the Literature Cited/Bibliography section, you
may use the following:
S Kumar, K Tamura, and M Nei (2004) MEGA3: Integrated software for Molecular
Evolutionary Genetics Analysis and sequence alignment. Briefings in Bioinformatics
5:150-163.
13
Part I: Getting Started
2 Part I: Getting Started
2.1 Installing MEGA
2.11 System Requirements
MEGA 4 was developed for use on Microsoft Windows® operating systems:
Windows 95/98, NT, ME, 2000, XP, or later. We recommend a computer with at
least 64 MB of RAM, 20 MB of available hard disk space, and an entry- level
Pentium® processor or equivalent. Our tests show that MEGA 4 runs well on
computers with an entry-level Pentium® CPU. However, for quick computation of
large datasets, you should have a faster processor and larger amount of physical
memory (RAM). MEGA 4 also can be run on other operating systems for which
Windows emulators are available.
Platform
Software
Macintosh
Windows using
VirtualPC
Sun Workstation
SoftWindows95
Linux
Windows using VMWare
2.12 Installing MEGA
The preferred way to install MEGA 4 is directly from the website
(www.megasoftware.net). A specially designed installation program automatically
downloads MEGA 4 and installs it in the location (directory) you specify.
If you are unable to install MEGA 4 directly from the website, you can download it
as a single compressed ZIP file. Then you must use a program, such as WinZip, to
uncompress this ZIP file in a temporary directory. Click on the MEGASETUP.EXE
file to install MEGA 4 on your computer automatically.
Finally, you may install MEGA 4 from a CD obtained from the authors. In this
case, insert the media into the computer and then click on MEGASETUP.EXE.
We recommend that you install MEGA 4 in one of the three ways described above.
Please do not simply copy MEGA 4-related files from one computer to another, as
MEGA 4 may not work properly if installed in this manner.
2.13 Uninstalling MEGA
The preferred way to uninstall programs in Windows is to use Add/Remove
Programs option in the control panel, which is accessible from the Start button on
14
Molecular Evolutionary Genetics Analysis
the lower left corner of your computer desktop. A dialog box (usually named
Add/Remove programs) will display a list of programs. To remove MEGA 4, scroll
down to MEGA 4 so that it is highlighted, then click Add/Remove.
2.2 Features & Support
2.21 What's New in Version 4
Version 4 contains a number of enhancements over MEGA 3.1. They include
Maximum Composite Likelihood method (MCL):
-
Estimation of evolutionary distances.
-
Construction of phylogenetic trees from MCL distances.
-
Inference of patterns of nucleotide substitution including estimation of
transition/transversion rate ratios (kappa1, kappa2), and the
transition/transversion rate bias (R).
Real-time Caption Expert Engine:
-
Generates publication-quality captions from analysis results.
-
Captions are generated dynamically based on the analysis options used, and
manual adjustments made within result explorers.
-
Captions for all result explorers available in MEGA.
-
Captions can be saved to a file, copied to an external program (Word, Excel,
etc), or printed directly from MEGA.
2.22 Feature List
1.0
DOS
2.x
Win
3.x
Win
4.x
Win
•
•
•
•
Manual editing of DNA and
Protein sequences
•
•
Motif searching/highlighting
•
•
MEGA Version
Platform
Input Data
DNA, Protein, Pairwise distance
matrix
Sequence Alignment Construction
Alignment Editor
15
Part I: Getting Started
Synchronous alignment
editing of original and translated
cDNA
•
•
Copy/Paste sequences
To/From Clipboard
•
•
Save alignment session for
future display
•
•
Ability to read sequencer,
MEGA, NEXUS, FASTA, and
other formats
•
•
Apply color/highlight
schemes to sequence data
•
•
Write alignment to MEGA
file for direct analysis in MEGA
•
•
BLAST sequences from
alignment directly
•
•
Complete native
implementation of ClustalW
•
•
Ability to select all options on
the fly
•
•
Ability to align any userselected region
•
•
Ability to align translated
cDNA sequences and automatic
adjustment
•
•
•
•
•
•
Mask vector (or any other
region)
•
•
Launch direct BLAST search
for whole or selected sequence
•
•
Send data directly to
Alignment Editor
•
•
Multiple Sequence Alignment
Sequencer (Trace) File editor/viewer
View ABI (*.abi, .ab1) and
Studfen (*.std?)
Edit trace file
16
Molecular Evolutionary Genetics Analysis
Integrated Web Browser and Sequence Fetching
Direct "usual" web and
GenBank browsing from MEGA
•
•
One-click sequence fetching
from databanks queries
•
•
Send sequence data from
BLAST search directly into
alignment
•
•
Bookmark favorite sequence
databank sites
•
•
Data Handling
Handling ambiguous states (R,Y,T,
etc.)
•
•
•
Extended MEGA format to save all
data attributes
•
•
•
Importing Data from other formats
(Clustal/Nexus/etc.)
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Data Explorers
Sequence
•
Distance matrix
Attributes supported
Groups of Sequences/Taxa
Domains
•
Genes and Mixed Domain
attributes
Explicit labels for sites
Automatic codon translation
•
•
•
•
Selection of codon positions
•
•
•
•
Selection of different site
categories
•
•
•
Visual Specification of
Domains/Groups
•
•
•
•
•
Center Analysis Preferences Dialog
17
Part I: Getting Started
Unlimited Data size for Analysis
•
•
•
•
•
•
•
•
•
Genetic Code Table Selection
Choose a desired table
•
Ability to add/edit user defined
tables
Computation of statistical attributes of a code table
Degeneracy of codon
positions
•
•
•
Numbers of potential
synonymous sites
•
•
•
Inclusion of all known code tables
•
•
•
Real-Time Caption Expert Engine
Generate Captions for Distance
Matrices
•
Generate Captions for Phylogenies
•
Generate Captions for Tests
•
Generate Captions for Alignments
•
Copy Captions to External
Programs
•
Save/Print Captions
•
Integrated Text File Editor
Unlimited Text File Size
•
•
•
Multi-file Tabbed Display
•
•
•
Columnar Block selection/Editing
•
•
•
Undo/Redo operations
•
•
•
Line numbers
•
•
•
Utilities to Format
Sequences/Reverse complement
etc.
•
•
•
Copy Screenshots to
EMF/WMF/Bitmap for
presentation
•
•
•
Sequence Data Viewer
18
Molecular Evolutionary Genetics Analysis
Two dimensional display of
molecular sequences
•
•
•
•
Display with identity symbol
•
•
•
Drag-drop sorting of sequences
•
•
•
Mixing coding and non-coding
sequence display
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
One-click translation
•
Display with all or only selected
taxa
Data Export
PAUP3, PHYLIP
•
PAUP4, PHYLIP Interleaved
Highlighting
0,2,4-fold degenerate sites
Variable, parsimony informative
sites
Constant Sites
Statistical Quantities estimation
DNA and protein sequence
compositions
•
Estimation by
genes/domains/groups
Codon Usage
•
Estimation by
genes/domains/groups
Use only
highlighted sites
MCL-based Estimation of Nucleotide Substitution Patterns
4x4 Rate Matrix
•
•
Transition/Transversion Rate
Ratios (k1, k2)
•
•
Transition/Transversion Rate Bias
(R)
•
•
19
Part I: Getting Started
Substitution Pattern Homogeneity Test
Composition Distance
•
•
•
Disparity Index
•
•
•
Monte-Carlo Test
•
•
•
Distance Estimation Methods
Nucleotide-by-Nucleotide
Models
No. of differences,
p-distance, Jukes-Cantor, Kimura
2P
•
•
•
•
Tajima-Nei,
Tamura 3-parameter, Tamura-Nei
distance
•
•
•
•
•
•
LogDet (TamuraKumar)
Maximum Composite Likelihood
•
Subcomponents
Transitions (ts),
tranversions (tv), ts/tv ratio
•
Number of common
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
sites
Account for rate variation
among sites
•
Relaxation of the
homogeneity assumption
Synonymous/Nonsynonymous (Codon-by-Codon)
Models
Nei-Gojobori (1986)
•
method
Modified Nei-Gojobori
method
Li-Wu-Lou, PBL, Kumar
method
Subcomponents
20
Molecular Evolutionary Genetics Analysis
Synonymous (s),
nonsynonymous (n) distances
•
•
•
•
Numbers of
synonymous and nonsynonymous
sites
•
•
•
Differences and
ratios (s-n, n-s, s/n, n/s)
•
•
•
4-fold degenerate
•
•
•
0-fold degenerate
•
•
•
Number of 0-fold
and 4-fold degenerate sites
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Between Group Average
•
•
•
Within Group Average
•
•
•
Net between group Average
•
•
•
Overall average
•
•
•
Mean Diversity within
Subpopulations
•
•
•
Mean Diversity for Entire
Populaton
•
•
•
Mean Interpopulational
Diversity
•
•
•
site distances
site distances
Protein distance
Number of differences, pdistance, Poisson
•
Dayhoff and JTT distances
Account for rate variation
among sites
•
Relaxation of the
homogeneity assumption
Distance Calculations
Pairwise
•
Sequence Diversity Calculations
21
Part I: Getting Started
Coefficient of Differentiation
•
•
•
•
•
•
•
•
•
Variance Calculations
Analytical
•
Bootstrap
Handling missing data
•
•
•
•
Automatic translation
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Within groups
•
•
•
Overall sequences
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Randomized tie-breaking in
bootstrapping
•
•
•
Minimum Evolution method
•
•
•
Branch-swapping (CloseNeighbor-Interchange; CNI)
•
•
•
Fast OLS computation
method
•
•
•
•
•
•
•
•
•
Automatic pasting of partial codons
between exons
Tests of Selection
Codon-based tests
Large sample Z-test
Between Sequences
Fisher's Exact Test
Tajima's Test of Neutrality
Molecular Clock Test
Tajima's relative rate test
Tree-making Methods
•
Neighbor-Joining
•
UPGMA
Randomized tie-breaking in
bootstrapping
Maximum Parsimony
22
Molecular Evolutionary Genetics Analysis
Nucleotide sequences
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Minimum Evolution
•
•
•
Maximum Parsimony
•
•
•
•
•
•
•
•
•
Protein sequences
Max-mini branch-and-bound
and min-mini searches
•
Branch-swapping (CNI)
Average branch length
estimation
Bootstrap Test of Phylogeny
Neighbor-joining/UPGMA
•
Confidence Probability Test
Neighbor-joining
•
Minimum Evolution
Consensus tree construction
•
•
•
•
Condensed tree construction
•
•
•
•
View pairwise distances
•
•
•
View between group distances
•
•
•
View within group distances
•
•
•
View distances and standard errors
simultaneously
•
•
•
Sort the distance matrix
•
•
•
Drag-and-drop
•
•
•
Group-wise
•
•
•
By Sequence names
•
•
•
Control display precision
•
•
•
Export Data for printing or reimporting
•
•
•
Distance Matrix Viewer
Tree Explorers
23
Part I: Getting Started
Phylogeny Display and Graphic
printing
•
•
•
•
On-the-spot taxa name editing
•
•
•
Multiple phylogeny views
•
•
•
Linearized Tree
•
•
•
Estimation of divergence time by
calibrating molecular clock
•
•
•
Copy to Clipboard/save to file as an
EMF drawing
•
•
•
Save to Newick format
•
•
Read trees from Newick format
•
•
•
•
•
Scale bar addition
•
•
•
Collapsing branches or
groups
•
•
•
Display only a subtree
•
•
•
Ability to view multiple trees
in different viewers
•
•
•
•
•
•
Add marker symbols to
names
•
•
•
Multi-color display and
printing
•
•
•
Vertical separation between
•
•
•
Horizontal size
•
•
•
Change Tree shape
•
•
•
Multiple tree display
•
•
•
Save tree session for future display
•
•
•
User specified control for
Placement and precision of
branch length
Tree Editing
Flipping, re-rooting
Change Tree Size
taxa
24
Molecular Evolutionary Genetics Analysis
What you see is what you get
printing
•
•
•
Multi- or single page printing
•
•
•
•
•
Display images on tree for groups
and taxa
2.23 Using MEGA in the Classroom
Because MEGA includes many statistical methods for the study of molecular
evolution in an interactive framework, it is instructive for classroom teaching. If
you are interested in using MEGA in the classroom, there are no restrictions. Your
students may download a copy from the website www.megasoftware.net or you
may install copies on multiple computers in a common computing area.
However, if you want to use MEGA in any other form, please contact the authors
by e-mail ([email protected]).
If you are using MEGA 4 in classroom teaching, please send us the following
information by e-mail for our records ([email protected]). (1) Your name, position
and institution, (2) course number and title, (3) number of students, and (4) course
semester and year.
2.24 Technical Support and Updates
All minor (bug fix) and major updates of MEGA will be made available at the
website www.megasoftware.net. We will send e-mail to all registered MEGA users
whenever an updated version of the program or the online help manual is made
available.
2.25 Reporting Bugs
If you encounter technical problems such as unexplained errors, documentation
inconsistencies, or program crashes, please report them to us by e-mail at
[email protected] For further information on reporting problems, consult
the bug report page on the MEGA website (www.megasoftware.net). Please note
that telephone inquiries will not be accepted.
Please include the following information in your report: (1) your name and
address, (2) the version of MEGA you are working with, (3) the version of
Windows you are working in, (4) a copy of your data file (if possible), (5) a
description of the problem, and (6) the sequence of events that led to that problem
25
Part I: Getting Started
[this often is crucial to understanding and remedying the problem quickly].
2.26 Guide to Notations Used
Item
Convention
Example
Directory &
file names
Small Cap +
Bold
INSTALL.TXT
File name
extensions
Small Cap +
Bold
.TXT, .DOC, .MEG
Email
address/URLs
Underlined
Pop-up help
links
Dotted
Underlined +
Green
statement
Help Jumps
Underlined +
Green
set of rules
Menu/Screen
Items
Italic
User-Entered
Text
Monospace
font
www.megasoftware.net
Data Menu
!Title
2.3 A Walk Through MEGA
Introduction to Walk Through MEGA
This section provides a MEGA tutorial. The data files for these examples can be
found in the EXAMPLES folder, located in the MEGA installation directory
(example in C:\Program Files\MEGA 4\Examples). In these example files, data
are deliberately written in different input formats. We recommend that you study
the examples in the order presented because the techniques explained in the initial
examples are used again in the subsequent ones.
In the following write-up, highlighted words indicate the keys you must press on
the keyboard. If you must press two keys simultaneously, they are shown with a +
sign between them (e.g., Alt + F3 means that the Alt and F3 keys should be
pressed simultaneously). Italicized letters are used to mark the commands found in
menus, submenus, and other locations as they appear on the computer screen. In
every example, we discuss many procedures introducing analytical techniques. For
ease of reference in later examples, these procedures are numbered in the Ex u.v.w
format, where u is the example number, v is the procedure number, and w is the
step number. For instance, Ex 1.3.2 refers to the 2nd step of the 3rd procedure in
example 1.
26
Molecular Evolutionary Genetics Analysis
A list of tutorials is as follows:
1. Aligning Sequences
2. Estimating Evolutionary Distances
3. Building Trees
4. Testing Tree Reliability
5. Marking Genes/Domains
6. Testing for Selection
7. Grouping Sequences
8. Computing Sequence Statistics
9. Trees from Distance Data
2.31 Creating Multiple Sequence Alignments
In this example, we will create an alignment from protein sequence data that will be imported into the alignment editor using
different methods.
Ex 1.0.1: Start MEGA 4 by double-clicking on the MEGA desktop icon or by using
the Windows start-menu to click on the MEGA icon located in the programs folder.
Ex 1.0.2: Launch the Alignment Explorer by selecting the Alignment|Alignment
Explorer/CLUSTAL menu command.
In order to aligning sequences contained in a Sequence Data File, do the following.
Ex 1.1.1: Add unaligned sequences from the hsp20.fas example file into the
Alignment Explorer by selecting the Data|Open|Get Sequences From File menu
command.
Ex 1.1.2: Select the Edit|Select All menu command to select every site for all
sequences in the alignment.
Ex 1.1.3: Select the Alignment|Align by ClustalW menu command to align the
selected sequences data using the ClustalW algorithm.
Ex 1.1.4: Save the current alignment session by selecting the Data|Save Session
menu item. This will allow the current alignment session to be restored for future
editing.
Ex 1.1.5: Exit the Alignment Explorer by selecting the Data|Exit Alignment
Explorer menu item. A message will appear asking if you would like to save the
data to a MEGA file. Choose "YES", and then a "Save As" dialog box will appear.
Enter hsp20_aligned.meg as the file name and click the "Save" button. An input box
will appear asking for a title for the data. Enter "HSP 20 Aligned by MEGA 4" as
27
Part I: Getting Started
the title and click the "OK" button. Another dialog will appear asking you if the
sequence data is protein coding. In this case click "Yes". A final dialog box will
appear asking you if you would like to open the data file in MEGA; click "Yes".
Now we examine how to send sequence data from the Internet (Web Explorer)
to the Alignment Explorer
Ex 1.2.1: If the Alignment Explorer already contains sequence data select the
Data|Create New menu command to create a new alignment. Choose "YES" for
each message that appears.
Ex 1.2.2: Activate the Web Explorer tab by selecting Web|Query Gene Banks from
the menu.
Ex 1.2.3: When the NCBI Entrez site is loaded, select either the nucleotide or
protein database, enter a search term into the search box, and press the "GO" button.
Ex 1.2.4: When the search results are displayed press the "Add to Alignment"
button located to the left of the address box. This will display the Web Fetch dialog
window.
Ex 1.2.5: Click the box to the left of each accession number whose sequences
information you would like to fetch from the web. When you are done selecting
accessions press the "Fetch" button.
Ex 1.2.6: When the status column indicates that all sequences are fetched press the
"Send to Alignment" button to send the fetched sequence data to the Alignment
Explorer.
Ex 1.2.7: Align the fetched data using the steps detailed in Ex 1.1.2 – Ex 1.1.5.
You may also open a trace file in the Trace Data Viewer/Editor and send it directly
to the Alignment Explorer.
2.32 Estimating Evolutionary Distances from Nucleotide Sequences
In this example, we will compute various distances for the Adh sequences from
11 Drosophila species. We will use the data from the previous example to
study various sequence statistics. In addition, we will see how these distances
can be written in a file in various formats through options for page size,
precision, and relative placement of distances and their standard errors.
Ex 2.0.1: Start MEGA 4 by double-clicking on the MEGA desktop icon or by
using the Windows start-menu to click on the MEGA icon located in the programs
folder.
Activate the data file Drosophila_Adh.meg using the instructions given in Ex 2.2.1
– Ex 2.2.3.
Now, we begin by computing the proportion of nucleotide differences between each
pair of Adh sequences.
Ex 2.1.1: Select the Distance|Compute Pairwise command (F7) to display the
28
Molecular Evolutionary Genetics Analysis
distance analysis preferences dialog box.
Ex 2.1.2: In the Distance Options tab, click the Models pulldown and then select
the Nucleotide|p-distance option.
Ex 2.1.3: You may look around at the other options but at this moment we will be
using the defaults for the remaining options. Click "Compute" to begin the
computation.
Ex 2.1.4: A progress indicator will appear briefly and then the distance
computation results will be displayed in grid form in a new window.
Now we will compute distances and compare them using other methods.
Ex 2.2.1: Select the Distance|Compute Pairwise command. Use the Models
pulldown to select the Nucleotide|Jukes-Cantor method. Now click "OK" to begin
the computation.
Ex 2.2.2: Follow the steps Ex. 2.1.1- Ex 2.1.3 and compute the Tamura Distance.
Ex 2.2.3: You now have open results windows containing the distances estimated
by three different methods, which you can now compare.
Ex 2.2.4: After you’ve compared the results, select the File|Quit Viewer option for
each result window.
We have computed nucleotide distances from the nucleotide sequence data in the file Drosophila_Adh.meg. Let us now
compute the proportion of amino acid differences. Note that MEGA will automatically translate the nucleotide sequences into
amino acid sequences using the selected genetic code table.
Ex 2.3.1: Select the Distance|Compute Pairwise command (F7) to display the
distance analysis preferences dialog box.
Ex 2.3.2: In the Distance Options tab, click the Models pulldown and then select
the Amino Acid|p-distance option.
Ex 2.3.3: Click the "OK" button to accept the default values for the rest of the
options and begin the computation.
Ex 2.3.4: A progress dialog box will appear briefly. As with the previous
nucleotide estimation a results viewer window will be displayed, showing the
distances in a grid format.
Ex 2.3.5: After you have inspected the results, use the File|Quit Viewer command
to close the results viewer. To shut down MEGA, select the File|Exit menu
command from the main MEGA application window and indicate that you would
like to close the data file.
2.33 Constructing Trees and Selecting OTUs from Nucleotide
Sequences
The Crab_rRNA.meg file contains nucleotide sequences for the large subunit
mitochondrial rRNA gene from different crab species (Cunningham et al.
29
Part I: Getting Started
1992). Since the rRNA gene is transcribed but not translated, it is in the
category of non-coding genes. Let us use this data file to illustrate the
procedures of building trees and in-memory sequence data editing, using the
commands present in the Data and Phylogeny menus.
Ex 3.0.1: Start MEGA 4 by double-clicking on the MEGA desktop icon or by
using the Windows start-menu to click on the MEGA icon located in the programs
folder.
Ex 3.1.1: Activate the data file Crab_rRNA.meg using the instructions given in Ex
2.2.1 - Ex 2.2.3.
Let us start by building a neighbor-joining tree.
Ex 3.2.1: Select the Phylogeny|Construct Tree|Neighbor-Joining command to
display the analysis preferences dialog box.
Ex 3.2.2: In the Options Summary tab, click the Model pulldown (found in the
Substitution Model section) and then select the Nucleotide|p-distance option.
Ex 3.2.3: Click "OK" to accept the defaults for the rest of the options and begin the
computations. A progress indicator will appear briefly, then the tree will be
displayed in the Tree Explorer.
Ex 3.2.4: To select a branch, click on it with the left mouse button. If you click on
a branch with the right mouse button, you will get a small options menu that will let
you flip the branch and perform various other operations on it. To edit the OTU
labels, double click on them.
Ex 3.2.5: Change the branch style by selecting the View|Tree/Branch Style
command from the Tree Explorer menu.
Ex 3.2.6: At this time the cursor assumes a triangular shape instead of the diamond
( ? ) shape. Press M and the mirror image of the original tree is displayed instantly.
Press M again and the tree reverts to its original shape.
Ex 3.2.7: Press the Up arrow key ( ) just once and the cursor moves upwards to
the next branch. Press F, the Flip command, and a mirror-like effect is produced on
the sub-tree anchored on the currently focused branch.
Ex 3.2.8: Select the View|Topology Only command from the Tree Explorer menu
and the branching pattern (without actual branch lengths) is displayed on the screen.
Press T again and the actual NJ tree reappears.
Ex 3.2.9: Press F1 to examine the help for tree editor. Use the help to become
familiar with the many operations that Tree Explorer is capable of performing.
Ex 3.2.10: DO NOT remove the tree from the screen. We shall use it for
illustrating how a tree can be printed.
Now you will print the NJ tree that you have on your screen in MEGA.
Ex 3.3.1: Select the File|Print command from the Tree Explorer menu to bring up
a standard Windows print dialog.
30
Molecular Evolutionary Genetics Analysis
Ex 3.3.2: To restrict the size of the printed tree to a single sheet of paper, choose
the File|Print in a Sheet command from the Tree Explorer menu.
Ex 3.3.3: Do not change anything in this dialog box. Select the Preview command
using the Tab key and a graphic image of the tree will be displayed on the screen.
Press Enter to return to the option box. Now go to the Write information option, and
select the Branch lengths. Again, select the Preview command (you may press Alt +
V).\ to show the tree drawn with branch lengths. Press Enter to come out of the
graphics image.
Ex 3.3.4: Select the File|Exit Tree Explorer (Ctrl-Q) command to exit the Tree
Explorer. A warning box will inform you that your tree data has not been saved.
Click the "OK" button to close Tree Explorer without saving the tree session.
In MEGA, you can also construct maximum parsimony (MP) trees. Let us
construct a maximum parsimony tree(s) by using the branch-&-bound search
option.
Ex 3.4.1: Select the Phylogeny|Construct Tree|Maximum Parsimony command. In
the resultant preferences window, choose the Max-Mini Branch-&-Bound Search
option in the MP Tree Search Options tab.
Ex 3.4.2: Click the "OK" button to accept the defaults for the other options and
begin the calculation. A progress window will appear briefly and the tree will be
displayed in Tree Explorer.
Ex 3.4.3: Now print this tree (See Ex 3.3.1 - 3.3.2). You do not have to specify the
printer name again because MEGA remembers your selection.
Ex 3.4.4: Select the File|Exit Tree Explorer (Ctrl-Q) command to exit the Tree
Explorer. A warning box will inform you that your tree data has not been saved.
Click "OK" to close Tree Explorer without saving the tree session.
Ex 3.4.5: Compare the NJ and MP trees. For this data set, the branching pattern of
these two trees is identical.
As an exercise, use the Heuristic Search for finding the MP tree. In this example, you will find the same tree as that
obtained by the branch-and-bound method if you use the default option (search factor equal to 2 for all steps of OTU
addition). However, the computational time will be much shorter. Actually, in this example, even a search factor equal
to 0 will recover the MP tree.
We will now examine how some data editing features work in MEGA. For
noncoding sequence data, OTUs as well as sites can be selected for analysis.
Let us remove the first OTU from the current data set.
Ex 4.1: Select the Data|Setup/Select Taxa & Groups command. A dialog box is
displayed.
Ex 4.2: All the OTU labels are checked in the left box. This indicates that all OTUs
are included in the current active data subset. To remove the first OTU from the
data, uncheck the checkbox next to the OTU name in the left pane.
Ex 4.3: Now from this data set construct a neighbor-joining tree (Ex 3.2.3) that
contains 12 OTUs instead of 13. To inactivate the operational data set and end the
current session of MEGA, press the hot-key Alt + X.
31
Part I: Getting Started
2.34 Tests of the Reliability of a Tree Obtained
In this example, we will conduct two different tests using protein-coding genes from the chloroplast genomes of nine different
species.
Ex 4.0.1: Start MEGA 4 by double-clicking on the MEGA desktop icon or by
using the Windows start-menu to click on the MEGA icon located in the programs
folder.
Activate the data in the Chloroplast_Martin.meg file by using the File|Open
command.
We will begin with the bootstrap test for the neighbor-joining tree.
Ex 4.1.1: Select the Tests|Bootstrap Test of Phylogeny|Neighbor Joining Tree
command from the main application menu.
Ex 4.1.2: An analysis preferences dialog box appears. Use the Models pulldown to
ensure that the Amino Acid|p-distance model is selected. Note that only the Amino
Acid submenu is available.
Ex 4.1.3: Click "OK" to accept the default values for the rest of the options. A
progress indicator provides the progress of the test as well as the details of your
analysis preferences.
Ex 4.1.4: Once the computation is complete, the Tree Explorer appears and display
two tree tabs. The first tab is the original Neighbor-Joining tree and the second is
the Bootstrap consensus tree.
Ex 4.1.5: To produce a condensed tree, use the Compute|Condensed Tree menu
command from the Tree Explorer menu. This tree shows all the branches that are
supported at the default cutoff value of BCL ≥ 50. To change this value, select the
View|Options menu command and click the cutoff values tab. Select the
Compute|Condensed Tree menu command and the NJ tree will reappear.
Ex 4.1.7: Print this tree. (see Ex 3.3.1 - Ex 3.3.2)
Ex 4.1.8: Select the File|Exit Tree Explorer (Ctrl-Q) command to exit the Tree
Explorer. A warning box will inform you that your tree data has not been saved.
Click "OK" to close Tree Explorer without saving the tree session.
For neighbor-joining trees, you may conduct the standard error test for every
interior branch by using the Phylogeny|Neighbor-Joining command. In
MEGA, this test is available for the p-distance, Poisson Correction and
Gamma distance for amino acid sequences. Since we did the above analysis
for the p-distance, we will use the same distance estimation method to
compare the results from the bootstrap and standard error tests.
Ex 4.2.1: Go to the Phylogeny menu and select the Neighbor-Joining command to
produce an analysis preferences dialog box. In the Models pulldown, be sure that pdistance is the model chosen. Click on the Test of Phylogeny tab to reveal the test
options. Under the Test of Inferred Phylogeny option group, check the Interior
32
Molecular Evolutionary Genetics Analysis
Branch Test option.
Ex 4.2.2: Click "OK" to begin the computation. A progress indicator will appear
briefly. The neighbor-joining tree with confidence probabilities (CP) from the
standard error test of branch lengths is displayed on the screen.
Ex 4.2.3: Compare the CP values on this tree with the BCL values of the tree that
you printed in the previous procedure.
Now exit MEGA using the Alt + X command.
2.35 Working With Genes and Domains
Ex 5.0.1: Start MEGA 4 by double-clicking on the MEGA desktop icon or by using
the Windows start-menu to click on the MEGA icon located in the programs folder.
Ex 5.0.2: Activate the data present in the Contigs.meg file by using the File|Open
command.
We will now examine how to define and edit gene and domain definitions
Ex 5.1.1: Select the Data|Setup/Select Genes & Domains menu command.
Ex 5.1.2: Delete the Data domain by right clicking on it and selecting Delete
Gene/Domain from the popup menu.
Ex 5.1.3: Right-click on the Genes/Domains item in the Names column and select
Add New Domain. Right-click on the new domain and select Edit Name from the
popup menu and set the name to "Exon1".
Ex 5.1.4: Select the ellipsis button next to the question mark in the From column to
set the first site of the domain. When the site selection window appears select site
number 1 and push the "OK" button.
Ex 5.1.5: Select the ellipsis button in the To column to set the last site of the
domain. When the site selection window appears select site number 3918 and push
the "OK" button.
Ex 5.1.6: Check the box in the Coding column to indicate that this domain is
protein coding.
Ex 5.1.7: Add two more domains to the Genes/Domains item. One of these
domains will be named "Intron1" and will begin at site 3919 and end at site 5191.
The other will be named "Exon2" and will begin at site 5192 and end at site 8421.
Be sure to check the checkbox in the Coding column for "Exon2" to indicate a
protein-coding domain.
Ex 5.1.8: Right-click on the Genes/Domains item and select Insert New Gene from
the popup menu. Change the name of this gene to "Predicted Gene" and click-anddrag all of the domains to this new gene such that they are displayed as children of
the "Predicted Gene" node in the display tree.
33
Part I: Getting Started
Ex 5.1.9: Press the "Close" button at the bottom of the window to exit the
Gene/Domain manager.
Now we use these domain definitions to restrict analyses when computing pairwise distances.
Ex 5.2.1: Select the Distances|Compute Pairwise menu item from the main menu
and make sure a Nucleotide model is selected.
Ex 5.2.2: On the Include Sites tab make sure that the "Noncoding sites" option does
not have a checkmark next to it. Go back to the main menu and press the
"Compute" button to begin the analysis.
Ex 5.2.3: When the computation is complete the Distance Explorer will display the
pairwise distance computed using only the sequence data from exonic domains of
the "Predicted Gene".
2.36 Test of Positive Selection
In this example, we present various analyses of protein-coding nucleotide sequences for five alleles from the human HLA-A
locus (Nei and Hughes 1991).
Ex 6.0.1: Start MEGA 4 by double-clicking on the MEGA desktop icon or by using
the Windows start-menu to click on the MEGA icon located in the programs folder.
Ex 6.1.1: Activate the data present in the HLA_3Seq.meg file by using the
File|Open command.
Ex 6.1.2: Now that the data file is active, note that various details about the data file
are displayed at the bottom of the main application window and more menu items
have become available on the main menu.
Let us compute the synonymous and nonsynonymous distances appropriate for studying positive Darwinian selection in this
set of antigen recognition codons.
Ex 6.2.1: Select the Tests|Codon-based Tests of Selection|Z-Test menu command.
An analysis preferences dialog appears. Use the Models pulldown in the Options
Summary tab to select Syn-Nonsysnonymous|Nei-Gojobori Method|p-distance
model. In the Test Selection tab, select Positive Selection from the pulldown and
select the Overall Average analysis type. Click the Include Sites tab and make sure
that the Pairwise Deletion option is selected.
Ex 6.2.2: Click on "OK" to accept the default values for the remaining options. A
progress indicator appears briefly; the computation results are displayed in a results
window in grid format.
Ex 6.2.3: The Prob column contains the probability computed (must be <0.05 for
hypothesis rejection at 5% level) and the Stat column contains the statistic used to
compute the probability. The difference in synonymous and nonsynonymous
substitutions should be significant at the 5% level.
Ex 6.2.4: Exit MEGA and deactivate the active data file using the Alt + X
command.
34
Molecular Evolutionary Genetics Analysis
2.37 Managing Taxa With Groups
Ex 7.0.1: Start MEGA 4 by double-clicking on the MEGA desktop icon or by using
the Windows start-menu to click on the MEGA icon located in the programs folder.
Ex 7.0.2: Activate the data present in the Crab_rRNA.meg file by using the
File|Open command.
We will now examine how to define and edit groups of taxa
Ex 7.1.2: Select the Data|Setup/Select Taxa & Groups menu command.
Ex 7.1.3: Press the "New Group" button found below the Taxa/Groups pane to add
a new group to the data. Name this new group "Pagurus".
Ex 7.1.4: While holding the Control button on the keyboard, click on all of the
Pagurus species in the Ungrouped Taxa pane to highlight them. When they are all
highlighted press the left-facing arrow button found on the vertical toolbar between
the two windowpanes.
Ex 7.1.5: Select the "All" group in the Taxa/Groups pane and press the "New
Group" button to add a second group. Name this group "Non-Pagurus". Add the
remaining unassigned taxa to this group and press the "Close" button at the bottom
of the window to exit this view.
Ex 7.1.6: Now that groups have been defined the Compute Within Group Mean,
Compute Between Group Means, and Compute Net Between Group Means menu
commands from the Distance menu item may be used to analyze the data.
2.38 Computing Statistical Quantities for Nucleotide Sequences
In this exercise, we illustrate the use of the Data Explorer for computing
various statistical quantities of nucleotide sequences. In addition, we explain
shortcuts for obtaining frequently used commands, methods of accessing online help, and the distinction between enabled and disabled commands.
Ex 8.0.1: Start MEGA 4 by double-clicking on the MEGA desktop icon or by using
the Windows start-menu to click on the MEGA icon located in the programs folder.
We now will examine the contents of the file Drosophila_Adh.meg by using the
built-in Text Editor.
Ex 8.1.1: Click on the File menu item to expand the menu options. To activate the
text editor either click on it or press the F3 key on your keyboard. In the text editor,
use the File|Open command to open the Drosophila_Adh.meg file.
Ex 8.1.2: Examine the Drosophila_Adh.meg file to reveal the #mega format
specifier, title, OTU names [what is an OTU?], and the interleaved sequence data.
Ex 8.1.3: We advise that you exit the text editor before proceeding with data
35
Part I: Getting Started
analysis. Select the File menu item from the text editor's menu and click the Exit
option from the expanded menu. If the editor asks you if you would like to save the
changes that you have made to the file, select No.
To study statistical quantities of the data in the file Drosophila_Adh.meg, we
must first activate it.
Ex 8.2.1: You can activate a data file using the link in the main application
window or select the File menu item from the main menu and click the Open Data
option from the expanded menu. You may also press the F5 key on your keyboard.
All of these methods will display a standard Windows open file dialog box.
Ex 8.2.2: Open the Drosophila_Adh.meg data file under the Examples folder.
Ex 8.2.3: A progress dialog box will appear briefly. When the data file is active,
details about it are displayed at the bottom of the main application window. More
menu items now are available on the main menu.
Examine the main menu. Now that the data file is active, the menu items Data,
Distances, and Tests have become available.
We now will use Data Explorer to compute some basic statistics for these data.
Ex 8.3.1: Select the Data|Data Explorer command or press the F4 key.
Ex 8.3.2: DNA sequences are displayed on the screen in a grid format. Use the
arrow keys (??) or the mouse to move from site to site; note a change in the bottomleft corner of the Site# display. Use the up and down (??) arrow keys or the mouse
to move between OTUs. The Total Sites view on the bottom-left panel displays the
sequence length and the Highlighted Sites displays "None" because no special site
attributes are yet highlighted.
Ex 8.3.3: To highlight variable sites, select the Highlight|Variable Sites option,
click the button labeled "V" from the shortcut bar below the menu, or press the V
key. All sites that are variable are highlighted, and the number in the Highlighted
Sites display changes. When you press V again, the sites return to the normal color
and Highlighted Sites displays "None."
Ex 8.3.4: Now to highlight the parsimony-informative, press the P key, click on
the button labeled "Pi" from the shortcut bar below the menu, or select the
Highlight/Parsim-info menu command. To highlight 0, 2, and 4-fold degenerate
sites, press the 0, 2, or 4 keys, respectively, click on the corresponding button from
the shortcut bar below the menu, or select the corresponding command from the
highlight menu.
Ex 8.3.5: To compute the nucleotide base frequencies, select the
Statistics|Nucleotide Composition menu command. This will calculate the
composition and display the results of the calculation in a text file using the built-in
text editor.
Ex 8.3.6: To compute codon usage, select the Statistics|Codon Usage menu
command. This will calculate the codon usage and display the results of the
calculation in a text file using the built-in text editor.
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Molecular Evolutionary Genetics Analysis
Ex 8.3.7: To compute nucleotide pair frequencies, select the Statistics|Nucleotide
Pair Frequencies|Directional or the Statistics|Nucleotide Pair
Frequencies|Unidirectional menu command. This will calculate the pair
frequencies and display the results of the calculation in a text file using the built-in
text editor.
Ex 8.3.8: To translate these protein-coding sequences into amino acid sequences
and back, press the T key or select the Data|Translate/Untranslate menu command
from the Data Explorer menu.
Ex 8.3.9: Once the sequences are translated, calculate the amino acid composition
by selecting the Statistics|Amino Acid Composition menu command from the Data
Explorer Menu.
Ex 8.3.10: To shut down MEGA, select the File|Exit menu command from the
main MEGA application window and close the data file.
2.39 Constructing Trees from Distance Data
This example introduces procedures for selecting options from menus, opening files in the read-only mode, activating
a distance data file, and building trees from the distance data.
Ex 9.0.1: Start MEGA by double-clicking on the MEGA desktop icon or by using
the Windows start-menu to click on the MEGA icon located in the programs folder.
Ex 9.0.2: A Splash screen appears, which displays the current version of MEGA.
Ex 9.0.3: This Splash screen automatically disappears and the MEGA application
becomes available.
In this example, we use the data in the Hum_Dist.meg file. Although we will
not edit the file, we will use MEGA's built-in text editor to examine its contents
before we proceed further.
Ex 9.2.1: Click on File menu to expand the menu options. Click on the menu item
labeled Text Editor or press on the F3 key to activate the built-in text editor.
Ex 9.2.2: Use the Text Editor to view the contents of the Hum_Dist.meg file. To
open a file with the Text Editor , click on the folder icon below the main menu or on
the File menu item, then choose Open from the expanded menu. You may also use
the key combination Ctrl+O to open a file. All of these options will lead you to a
standard Windows open file dialog box. Use this dialog box to locate the Examples
folder and open the Hum_Dist.meg file. After you open the file with the dialog
box, you will see the file contents displayed in the Text Editor window.
Ex 9.2.3: Examine the contents of the data file and then exit the Text Editor before
proceeding with data analysis. Select the File menu item from the Text Editor ‘s
menu and click Exit from the expanded menu. If the editor asks you if you would
like to save your changes, select No.
A data file must be activated before an analysis can be performed. (Note that opening a file for browsing or editing is
different from activating it for analysis.) Now we will activate the Hum_Dist.meg data file.
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Part I: Getting Started
Ex 9.3.1: You can activate a data file by using the link in the main application
window or by selecting the File menu item from the main menu and clicking the
Open Data option from the expanded menu. You also can press the F5 key on your
keyboard. All of these methods will display a standard Windows open file dialog
box.
Ex 9.3.2: Use the open file dialog box to locate and open the Hum_Dist.meg file
located in the Examples folder. After you have selected the file for opening, a
progress indicator will appear briefly.
Ex 9.3.3: When the data file is active, details about it are displayed at the bottom of
the main application window. More menu items now are available on the main
menu.
Now we will make a phylogenetic tree from the distance data.
Ex 9.4.1: From the expanded menu in the Phylogeny menu, select the Neighborjoining command.
Ex 9.4.2: A confirmation window will appear, indicating that MEGA is ready to
conduct the requested analysis. Click on the button labeled "OK;" a progress meter
will appear briefly.
Ex 9.4.3: The Tree Explorer will instantly display a neighbor-joining tree on the
screen. To exit the Tree Explorer, select the File menu item from the Tree Explorer
menu and click the Exit Tree Explorer option from the expanded menu. The Tree
Explorer will ask you if you would like to save the tree data. If you save the tree,
you can use Tree Explorer to view and manipulate it in the future.
With this, let us end this session of MEGA.
Ex 9.5.1: Go to the Data menu and click on the Close Data command. The
program will inquire if you would like the data to be inactivated. Select "Yes."
Ex 9.5.2: To exit MEGA, press Alt + X, or select the Exit command from the
expanded File menu.
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Molecular Evolutionary Genetics Analysis
3 Part II: Assembling Data for Analysis
Text File Editor and Format Converter
MEGA includes a Text File Editor, which is useful for creating and editing ASCII
text files. It is invoked automatically by MEGA if the input data file processing
modules detect errors in the data file format. In this case, you should make
appropriate changes and save the data file.
The text editor is straightforward if you are familiar with programs like Notepad.
Click on the section you wish to change, type in the new text, or select text to cut,
copy or paste. Only the display font can be used in a document. You can have as
many different text editor windows open at one time and you may close them
independently. However, if you have a file open in the Text Editor, you should
save it and close the Text Editor window before trying to use that data file for
analysis in MEGA. Otherwise, MEGA may not have the most up-to-date version of
the data.
The Text File Editor and Format converter is a sophisticated tool with numerous
special capabilities that include:
•
Large files –The ability to operate on files of virtually unlimited size and
line lengths.
•
General purpose –Used to view/edit any ASCII text file.
•
Undo/ReDo –The availability of an unlimited depth of undo/redo options
•
Search/Replace –Searches for and does block replacements for arbitrary
strings.
•
Clipboard – Supports familiar clipboard cut, copy, and paste operations.
•
Normal and Column blocks – Supports regular contiguous line blocks and
columnar blocks. This is quite useful while manually aligning sequences in
the Text Editor.
•
Drag/Drop – Moves text with the familiar cut and paste operations or you
can select the text and then move it with the mouse.
•
Screenshots –Creates screen snapshots for teaching and documentation
purposes directly from the edit window.
•
Printing –Prints the contents of the edit file.
The Text Editor contains a menu bar, a toolbar, and a status bar.
The Menu bar
Menu
Description
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Part II: Assembling Data for Analysis
File
menu
The File Menu contains the functions
that are most commonly used to open,
save, rename, print, and close files.
(Although there is no separate
"rename" function available, you can
rename a file by choosing the Save
As… menu item and giving the file a
different name before you save it.)
Edit
menu
The Edit Menu contains functions that
are commonly used to manipulate
blocks of text. Many of the edit menu
items interact with the Windows
Clipboard, which is a hidden window
that allows various selections to be
copied and pasted across documents
and applications.
Search
menu
The Search Menu has several functions
that allow you to perform searches and
replacements of text strings. You can
also jump directly to a specific line
number in the file.
Display
menu
The Display Menu contains functions
that affect the visual display of files in
the edit windows.
Utilities
menu
The Utilities Menu contains several
functions that make this editor
especially useful for working with files
containing molecular sequence data
(note that the MEGA 4 editor does not
try to understand the contained data, it
simply operates on the text, assuming
that the user knows what (s)he is doing.
Toolbar
The Toolbar contains shortcuts to some frequently used menu commands.
Status Bar
The Status bar is positioned at the bottom of the editor window. It shows the
position of the cursor (line number and position in the line), whether the file has
been edited, and the status of some keyboard keys (CAPS, NUM, and SCROLL
lock).
Hotkeys and Shortcut keys
Many menu items have a hotkey and/or a shortcut key. These are special key
combinations that are helpful for people who are more comfortable using a
keyboard than the mouse. Hotkeys are identified by an underscore character in the
name of the menu item, e.g., "File", "New". These allow you to hold down the
Alt-key, which is usually found next to the space bar on the keyboard, then hit the
underlined letter to produce the same action as if you clicked that name with the
mouse. We show this using the notation <Alt>+key – e.g., the hotkey for the file
menu item is shown as <Alt>+F. Be sure that you depress both keys together,
holding the <Alt> key down a little bit longer than the letter key. (Some people try
40
Molecular Evolutionary Genetics Analysis
hitting both keys simultaneously, as if they’re hitting two keys on a piano
keyboard. Quite often, this approach does not produce the desired results.)
For instance, you could create a new file by clicking the mouse on the "File"
menu item, then clicking on the "New" item beneath it. Using hotkeys, you could
type <Alt>+F followed by <Alt>+N. Or, more simply, while you’re holding down
the <Alt> key, hit the ‘F’ key followed by the ‘N’ key, then release the <Alt> key.
You might notice that several menu items, e.g., the New Item on the File
menu, show something to the right that looks like ‘Ctrl+N’. This is called a
Shortcut key sequence. Whereas executing a command with hotkeys often requires
several keystrokes, shortcut keys can do the same thing with just one keystroke.
Shortcut keys work the same as hotkeys, using the <Ctrl> key instead of the <Alt>
key. To create a new file, for example, you can hold down the <Ctrl> key and hit
the ‘N’ key, which is shown as <Ctrl>+N here. (In the menus, this appears simply
as ‘Ctrl+N’.)
Not all menu items have associated shortcut keys because there are only 26
shortcut keys, one for each letter of the alphabet. Hotkeys, in contrast, are
localized to each menu and submenu. For hotkeys to work, the menu item must be
visible whereas shortcut keys work at any time. For instance, if you are typing data
into a text file and want to create a note in a new window, you may simply hit the
shortcut key sequence, <Ctrl>+N to generate a new window. After you type the
note, you can hit <Ctrl>+S to save it, give it a file name, hit the enter key [this part
doesn’t make sense]; then you can hit the <Alt>+F+C hotkey sequence to close the
file (there is no shortcut key for closing a file).
3.1 Trace Data File Viewer/Editor
Using this function, you can view and edit trace data produced by an automated
DNA sequencer in ABI and Staden file formats. The sequences displayed can be
added directly into the Alignment Explorer or sent to the Web Browser for
conducting BLAST searches.
A brief description of various functions available in the Trace Data file
Viewer/Editor are as follows:
Data menu
Open File in New Window: Launches a new instance to view/edit another file.
Open File: Allows you to select another file to view/edit in the current window.
Save File: Save the current data to a file in Staden format.
Print: Prints the current trace data, excluding all masked regions.
Add to Alignment Explorer: DNA sequence data, excluding all masked
regions, is sent to the Alignment Explorer and appears as a new sequence at the
end of the current alignment.
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Part II: Assembling Data for Analysis
Exit: Closes the current window.
Edit menu
Undo: Use this command to Undo one or more previous actions.
Copy: This menu provides options to (1) copy DNA sequences from FASTA or
plain text formats to the clipboard and (2) copy the exact portion you are viewing
of the currently displayed trace image to the clipboard in the Windows
Enhanced Meta File format. For FASTA format copying, both the sequence
name and the DNA data will be copied, excluding the masked regions. To copy
only the selected portion of the sequence, use the plain text copy command (If
nothing is selected, then the plain text command will copy the entire sequence,
except for the masked regions).
Mask Upstream: Mask or unmask region to the left (upstream) of the cursor.
Mask Downstream: Mask or unmask region to the right (downstream) of the
cursor.
Reverse Complement: Reverse complements the entire sequence.
Search menu
Find: Finds a specified query sequence.
Find Next: Finds the next occurrence of the query sequence. To specify the
query sequence, first use the Find menu command.
Find Previous: Finds the previous occurrence of a query sequence. To specify
the query sequence, first use the Find command.
Next N: Go to the next indeterminate (N) nucleotide.
Search in File: This command searches another file, which you specify, for the selected
sequence in the current window. It can be used when you are assembling sequence
subclones to build a contig.
Do BLAST Search: Launch web browser to BLAST the currently selected
sequence. If nothing is selected, the entire sequence, excluding the masked
regions, will be used.
3.2 Web Browser
MEGA contains a fully functional Web Browser to assist users in sequence data retrieval and web
exploration. The most important feature that differentiates this web browser from other browsers (e.g.,
Netscape or Internet Explorer) is the
button. Pressing this button causes the MEGA
web explorer to extract sequence data from the currently displayed web page and send it to the
Alignment Explorer ’s alignment grid, where it will be inserted as new sequences. At present, the
MEGA web browser can interpret data displayed in FASTA format or in the default format at the NCBI
website. (You can ask the NCBI website to display the data in the FASTA format by using the
Display option on the web page shown.) (We plan to enhance this functionality further in version 3.1.)
42
Molecular Evolutionary Genetics Analysis
Furthermore, the MEGA web browser provides a genomics database, exploration oriented interface for
web searching. (In fact this is almost the same functionality as in the most recent versions of the
Internet Explorer.)
This causes the web browser window to navigate back to the web
location found before the current site in the explorer location
history.
This causes the web browser window to navigate forward to the
web location found after the current site in the explorer location
history.
This causes the web browser to terminate loading a web location.
This causes the web browser to reload the current web location.
This causes the web browser to extract sequence data from the
current web page and send it to Alignment Builder’s alignment
grid as new sequence rows. If the web explorer is unable to find
properly formatted sequence data in the current web page a
warning box will appear.
Address
Field
Links
The web location, or address field, is located in the second toolbar.
This field contains the URL of the current web location as well as
a pull down list of previously visited URLs. If a new URL is
entered into the box and the Enter key is pressed, the web explorer
will attempt to navigate to the entered URL.
This toolbar provides shortcuts to a selection of websites.
There are number of menus in the web browser, including Data, Edit, View,
Links, Go, and Help. These menus provide access to routine functionalities,
which are self-explanatory in use.
3.3 Some Text Editor Utilities
3.31 Open Saved Alignment Session
Alignment | Open Saved Alignment Session…
Use this command to display a previously saved Alignment Explorer session
(saved in a filename with .MAS extension).
3.32 Copy Screenshot to Clipboard
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Part II: Assembling Data for Analysis
Utilities | Copy Screenshot to Clipboard
This item presents three other options for selecting the format of an image that is
being copied to the clipboard. Once it is copied, it can be pasted in any other
graphic or word processing program.
Bitmap Format: This is the common Windows Bitmap (BMP) Format.
Windows Metafile Format: This selects the Windows Metafile Format (WMF)
Enhanced Metafile Format: This selects the Windows Enhanced Metafile Format.
3.33 Format Selected Sequence
Utilities | Convert to Mega Format
This submenu presents four other menu items that offer some common ways of
reformatting text.
Merge Multiple Lines: This is used to merge several separate lines into one long
(very wide) line
Remove Spaces/Digits: This is used to remove spaces and digits from a genetic
sequence.
Insert Spaces Every 3: This is used to break the selected text into three-character
chunks (e.g., codons). Note that it does not remove any already existing spaces.
Insert Spaces Every 10: This is used to break the selected text into ten-character
chunks.
3.34 Reverse Complement
Utilities | Reverse Complement
This item reverses the order of characters in the selected block and then replaces
each character by its complement. Only A, T, U, C, and G are complemented; the
rest of the characters are left as they are. Please use it carefully as MEGA 4 does
not validate whether the characters in the selected block are nucleotides.
3.35 Convert to Mega Format (in Text Editor)
Utilities | Convert to Mega Format
This item converts the sequence data in the current edit window, or in a selected
file, into a MEGA format file. It brings up a dialog box, which allows you to
choose the file and/or the format for this purpose. MEGA 4 converts the data file
and displays the converted data in the editor.
Files written in a number of popular data formats can be converted into MEGA
44
Molecular Evolutionary Genetics Analysis
format. MEGA 4 supports the conversion of CLUSTAL, NEXUS (PAUP,
MacClade), PHYLIP, GCG, FASTA, PIR, NBRF, MSF, IG, and XML formats.
Details about how MEGA reads and converts these file formats are given in the
section Importing Data from Other Formats.
3.4 Building Sequence Alignments
3.41 Alignment Explorer
The Alignment Explorer provides options to (1) view and manually edit alignments
and (2) generate alignments using a built-in CLUSTALW implementation (for the
complete sequence or data in any rectangular region). The Alignment Explorer
also provides tools for exploring web-based databases (e.g., NCBI Query and
BLAST searches) and retrieving desired sequence data directly into the current
alignment.
The Alignment Explorer has the following menus in its main menu: Data, Edit,
Search, Alignment, Web, Sequencer, Display, and Help. In addition, there are
Toolbars that provide quick access to many Alignment Explorer functions. The main
Alignment Explorer window contains up to two alignment grids.
For amino acid input sequence data, the Alignment Explorer provides only
one view. However, it offers two views of DNA sequence data: the DNA
Sequences grid and the Translated Protein Sequences grid. These two views are
present in alignment grids in the two tabs with each grid displaying the sequence
data for the current alignment. Each row represents a single sequence and each
column represents a site. A "*" character is used to indicate site columns, exhibiting
consensus across all sequences. An entire sequence may be selected by clicking on
the gray sequence label cell found to the left of the sequence data. An entire site
may be selected by clicking on the gray cell found above the site column. The
alignment grid has the ability to assign a unique color to each unique nucleotide or
amino acid and it can display a background color for each cell in the grid. This
behavior can be controlled from the Display menu item found in the main menu.
Please note that when the ClustalW alignment algorithm is initiated, it only will
align the sites currently selected in the alignment grids. Multiple sites may be
selected by clicking and then dragging the mouse within the grid. Note that all of
the manual or automatic alignment procedures carried out in the Protein Sequences
grid will be imposed on the corresponding DNA sequences as soon as you flip to the
DNA sequence grid. Even more importantly, the Alignment Explorer provides
unlimited UNDO capabilities.
3.42 Aligning coding sequences via protein sequences
MEGA 3 provides a convenient method for aligning coding sequences based on the
alignment of protein sequences. In order to accomplish this you use the Alignment
Explorer to load a data file containing protein-coding sequences. If you click on
45
Part II: Assembling Data for Analysis
the Translated Protein Sequences tab you will see that the protein-coding
sequences are automatically translated into their respective protein sequence. With
this tab active select the Alignment|Align by ClustalW menu item or click on the
"W" tool bar icon to begin the alignment of the translated protein sequences. Once
the alignment of the translated protein sequences completes, click on the DNA
Sequences tab and you’ll find that Alignment Explorer automatically aligned the
protein-coding sequences according to the aligned translated protein sequences.
Any manual adjustments made to the translated protein sequence alignment will
also be reflected in the protein-coding sequence tab.
3.43 CLUSTALW
About CLUSTALW
ClustalW is a widely used system for aligning any number of homologous
nucleotide or protein sequences. For multi-sequence alignments, ClustalW uses
progressive alignment methods. In these, the most similar sequences, that is, those
with the best alignment score, are aligned first. Then progressively more distant
groups of sequences are aligned until a global alignment is obtained. This
heuristic approach is necessary because finding the global optimal solution is
prohibitive in both memory and time requirements. ClustalW performs very well
in practice. The algorithm starts by computing a rough distance matrix between
each pair of sequences based on pairwise sequence alignment scores. These scores
are computed using the pairwise alignment parameters for DNA and protein
sequences. Next, the algorithm uses the neighbor-joining method with midpoint
rooting to create a guide tree, which is used to generate a global alignment. The
guide tree serves as a rough template for clades that tend to share insertion and
deletion features. This generally provides a close-to-optimal result, especially
when the data set contains sequences with varied degrees of divergence, so the
guide tree is less sensitive to noise.
See:
Higgins D., Thompson J., Gibson T. Thompson J. D., Higgins D. G., Gibson T. J.
CLUSTAL W: improving the sensitivity of progressive multiple sequence
alignment through sequence weighting, position-specific gap penalties and weight
matrix choice.
Nucleic Acids Res. 22:4673-4680. (1994)
CLUSTALW Options (DNA)
This dialog box displays a single tab containing a set of organized parameters that
are used by ClustalW to align the DNA sequences. If you are aligning proteincoding sequences, please note that CLUSTALW will not respect the codon
positions and may insert alignment gaps within codons. For aligning cDNA or
sequence data containing codons, we recommend that you align the translated
protein sequences (see Aligning coding sequences via protein sequences).
46
Molecular Evolutionary Genetics Analysis
In this dialog box, you will see the following options:
Parameters for Pairwise Sequence Alignment
Gap Opening Penalty: The penalty for opening a gap in the alignment.
Increasing this value makes the gaps less frequent.
Gap Extension Penalty: The penalty for extending a gap by one residue.
Increasing this value will make the gaps shorter. Terminal gaps are not
penalized.
Parameters for Multiple Sequence Alignment
Gap Opening Penalty: The penalty for opening a gap in the alignment.
Increasing this value makes the gaps less frequent.
Gap Extension Penalty: The penalty for extending a gap by one residue.
Increasing this value will make the gaps shorter. Terminal gaps are not
penalized.
Common Parameters
DNA Weight Matrix: The scores assigned to matches and mismatches (including
IUB ambiguity codes).
Transition Weight: Gives transitions a weight between 0 and 1. A weight of zero
means that the transitions are scored as mismatches, while a weight of 1 gives the
transitions the match score. For distantly-related DNA sequences, the weight
should be near zero; for closely-related sequences, it can be useful to assign a
higher score.
Use Negative Matrix: Enabled negative weight matrix values will be used if they
are found; otherwise the matrix will be automatically adjusted to all positive
values.
Delay Divergent Cutoff (%): Delays the alignment of the most distantly-related
sequences until after the most closely-related sequences have been aligned. The
setting shows the percent identity level required to delay the addition of a
sequence. Sequences that are less identical than this level will be aligned later.
Keep Predefined Gaps: When checked, alignment positions in which ANY of the
sequences have a gap will be ignored.
NOTE: All Definitions are derived from the CLUSTALW manual.
CLUSTALW Options (Protein)
This dialog box displays a single tab containing a set of organized parameters that
are used by ClustalW to align DNA sequences. If you are aligning protein-coding
sequences, please note that CLUSTALW will not respect the codon positions and
may insert alignment gaps within codons. For aligning cDNA or sequence data
containing codons, we recommend that you align the translated protein sequences
(see Aligning coding sequences via protein sequences).
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Part II: Assembling Data for Analysis
In this dialog box, you will see the following options:
Parameters for Pairwise Sequence Alignment
Gap Opening Penalty: The penalty for opening a gap in the alignment.
Increasing this value makes the gaps less frequent.
Gap Extension Penalty: The penalty for extending a gap by one residue.
Increasing this value will make the gaps shorter. Terminal gaps are not
penalized.
Parameters for Multiple Sequence Alignment
Gap Opening Penalty: The penalty for opening a gap in the alignment.
Increasing this value makes the gaps less frequent.
Gap Extension Penalty: The penalty for extending a gap by one residue.
Increasing this value will make the gaps shorter. Terminal gaps are not
penalized.
Common Parameters
DNA Weight Matrix: The scores assigned to matches and mismatches (including
IUB ambiguity codes).
Residue-specific Penalties: Amino acid specific gap penalties that reduce or
increase the gap opening penalties at each position or sequence in the alignment.
For example, positions that are rich in glycine are more likely to have an adjacent
gap than positions that are rich in valine. See the documentation for details.
Hydrophilic Penalties: Used to increase the chances of a gap within a run (5 or
more residues) of hydrophilic amino acids; these are likely to be loop or random
coil regions in which gaps are more common.
Gap Separation Distance: Tries to decrease the chances of gaps being too close to
each other. Gaps that are less than this distance apart are penalized more than
other gaps. This does not prevent close gaps; it makes them less frequent,
promoting a block-like appearance of the alignment.
Use Negative Matrix: When enabled negative weight matrix values will be used
if they are found; otherwise the matrix will be automatically adjusted to all positive
values.
Delay Divergent Cutoff (%): Delays the alignment of the most distantly-related
sequences until after the alignment of the most closely-related sequences. The
setting shows the percent identity level required to delay the addition of a
sequence; sequences that are less identical than this level will be aligned later.
Keep Predefined Gaps: When checked, any alignment positions in which ANY of
the sequences have a gap will be ignored.
NOTE: All definitions are derived from CLUSTALW manual.
48
Molecular Evolutionary Genetics Analysis
3.44 BLAST
About BLAST
BLAST is a widely used tool for finding matches to a query sequence within a
large sequence database, such as Genbank. BLAST is designed to look for local
alignments, i.e. maximal regions of high similarity between the query sequence and
the database sequences, allowing for insertions and deletions of sites. Although the
optimal solution to this problem is computationally intractable, BLAST uses
carefully designed and tested heuristics that enable it to perform searches very
rapidly (often in seconds). For each comparison, BLAST reports a goodness score
and an estimate of the expected number of matches with an equal or higher score
than would be found by chance, given the characteristics of the sequences. When
this expected value is very small, the sequence from the database is considered a
"hit" and a likely homologue to the query sequence. Versions of BLAST are
available for protein and DNA sequences and are made accessible in MEGA via the
Web Browser.
See:
Altschul, S.F., Gish, W., Miller, W., Myers, E.W. & Lipman, D.J. (1990) "Basic
local alignment search tool." J. Mol. Biol. 215:403-410.
Do BLAST Search
Alignment | Do BLAST Search
Use this to launch the BLAST search in the MEGA Web Browser. The web-browser is
displayed with the BLAST facility at the NCBI website.
3.45 Menu Items in the Alignment Explorer
Toolbars in Alignment Explorer
Basic Functions
This prepares Alignment Builder for a new alignment. Any sequence data
currently loaded into Alignment Builder is discarded.
This activates the Open File dialog window. It is used to send sequence data
from a properly formatted file into Alignment Builder.
This activates the Save Alignment Session dialog window. It may be used to
save the current state of the Alignment Builder into a file so that it may be
restored in the future.
This causes nucleotide sequences currently loaded into Alignment Builder to
be translated into their respective amino acid sequences.
49
Part II: Assembling Data for Analysis
Web/Data Explorer Functions
This displays the NCBI BLAST web site in the Web Explorer tab window. If
a sequence in the sequence grid is selected prior to clicking this button, the
Web Explorer will auto-fill the BLAST query window with the selected
sequence data.
This displays the default database (GenBank) in the Web Explorer tab
window.
This activates the Open Trace File dialog window, which may be used to
open and view a sequencer file. The sequence data from the sequencer file
then can be sent into Alignment Explorer.
Alignment Functions
This displays the ClustalW parameters dialog window, which is used to
configure ClustalW and initiate the alignment of the selected sequence data.
If you do not select sequence data prior to clicking this button, a message
box will appear asking if you would like to select all of the currently loaded
sequences.
This marks or unmarks the currently selected single site in the alignment
grid. Each sequence in the alignment may have only one site marked at a
time. Modifications can be made to the alignment by marking two or more
sites and then aligning them using the Align Marked Sites function.
This button aligns marked sites. Two or more sites must be marked in order
for this function to have an effect.
Search Functions
This activates the Find Motif search box. When this box appears, it asks you
to enter a motif sequence (a small subsequence of a larger sequence) as the
search term. After the search term is entered, the Alignment Builder finds
each occurrence of the search term and indicates it with yellow highlighting.
For example, if you were to enter the motif "AGA" as the search term, then
each occurrence of "AGA" across all sequences in the sequence grid would
be highlighted in yellow.
This searches towards the beginning of the current sequence for the first
occurrence of the motif search term. If no motif search has been performed
prior to clicking this button, the Find Motif search box will appear.
This searches towards the end of the current sequence for the first occurrence
of the motif search term. If no motif search has been performed prior to
clicking this button, the Find Motif search box will appear.
This locates the marked site in the current sequence. If no site has been
marked, a warning box will appear.
Editing Functions
This undoes the last Alignment Builder action.
50
Molecular Evolutionary Genetics Analysis
This copies the current selection to the clipboard. It may be used to copy a
single base, a block of bases, or entire sequences to the clipboard.
This removes the current selection from the Alignment Builder and sends it
to the clipboard. This function can affect a single base, a block of bases, or
entire sequences.
This pastes the contents of the clipboard into the Alignment Builder. If the
clipboard contains a block of bases, it will be pasted into the builder starting
at the point of the current selection. If the clipboard contains complete
sequences they will be added to the current alignment. For example, if the
contents of a FASTA file were copied to the clipboard from a web browser,
it would be pasted into Alignment Builder as a new sequence in the
alignment.
This deletes a block of selected bases from the alignment grid.
This deletes gap-only sites (sites containing a gap across all sequences in the
alignment grid) from a selected block of bases.
Sequence Data Insertion Functions
This creates a new, empty sequence row in the alignment grid. A label and
sequence data must be provided for this new row.
This activates an Open File dialog box that allows for the selection of a
sequence data file. Once a suitable sequence data file is selected, its contents
will be imported into Alignment Builder as new sequence rows in the
alignment grid.
Site Number display on the status bar
Site #
The Site # field indicates the site represented by the current selection. If the
w/o Gaps radio button is selected, then the Alignment Builder will disregard
the shifting affect of gaps when determining gap sites. If a block of sites are
selected, then this field will contain the site # for the first site in the block. If
an entire sequence is selected this field will contain the site # for the last site
in the sequence.
Alignment Menu (in Alignment Explorer)
This menu provides access to commands for editing the sequence data in the alignment
grid. The commands are:
Align by ClustalW: This option is used to align the DNA or protein sequence included in
the current selection on the alignment grid. You will be prompted for the alignment
parameters (DNA or Protein) to be used in ClustalW; to accept the parameters, press "OK".
This initiates the ClustalW alignment system. Alignment Builder then aligns the current
selection in the alignment grid using the accepted parameters.
Mark/Unmark Site: This marks or unmarks a single site in the alignment grid. Each
sequence in the alignment may only have one site marked at a time. Modifications can be
51
Part II: Assembling Data for Analysis
made to the alignment by marking two or more sites and then aligning them using the
Align Marked Sites function.
Align Marked Sites: This aligns marked sites. Two or more sites in the alignment must be
marked for this function to have an effect.
Unmark All Sites: This item unmarks all currently marked sites across all sequences in the
alignment grid.
Delete Gap-Only Sites: This item deletes gap-only sites (site columns containing gaps
across all sequences) from the alignment grid.
Auto-Fill Gaps: If this item is checked, then the Alignment Builder will ensure that all
sequences in the alignment grid are the same length by padding shorter sequences with
gaps at the end.
Display Menu (in Alignment Explorer)
This menu provides access to commands that control the display of toolbars in the
alignment grid. The commands in this menu are:
Toolbars: This contains a submenu of the toolbars found in Alignment Explorer. If
an item is checked, then its toolbar will be visible within the Alignment Explorer
window.
Use Colors: If checked, Alignment Explorer displays each unique base using a
unique color indicating the base type.
Background Color: If checked, then Alignment Explorer colors the background of
each base with a unique color that represents the base type.
Font: The Font dialog window can be used to select the font used by Alignment
Explorer for displaying the sequence data in the alignment grid.
Edit Menu (in Alignment Explorer)
This menu provides access to commands for editing the sequence data in the
alignment grid. The commands in this menu are:
Undo: This undoes the last Alignment Explorer action.
Copy: This copies the current selection to the clipboard. It may be used to copy a
single base, a block of bases, or entire sequences.
Cut: This removes the current selection from the Alignment Explorer and sends it
to the clipboard. This function can affect a single base, a block of bases, or entire
sequences.
Paste: This pastes the contents of the clipboard into the Alignment Explorer. If the
clipboard contains a block of bases, they will be pasted into the builder, starting at
the point of the current selection. If the clipboard contains complete sequences,
they will be added to the current alignment. For example, if the contents of a
FASTA file are copied from a web browser to the clipboard, they will be pasted
52
Molecular Evolutionary Genetics Analysis
into the Alignment Explorer as a new sequence in the alignment.
Delete: This deletes a block of selected bases from the alignment grid.
Delete Gaps: This deletes gaps from a selected block of bases.
Insert Blank Sequence: This creates a new, empty sequence row in the alignment
grid. A label and sequence data must be provided for this new row.
Insert Sequence From File: This activates an Open File dialog box that allows for
the selection of a sequence data file. Once a suitable sequence data file is selected,
its contents will be imported into Alignment Explorer as new sequence rows in the
alignment grid.
Select Site(s): This selects the entire site column for each site within the current
selection in the alignment grid.
Select Sequences: This selects the entire sequence for each site within the current
selection in the alignment grid.
Select all: This selects all of the sites in the alignment grid.
Allow Base Editing: If this item is checked, it changes the base values for all cells
in the alignment grid. If it is not checked, then all bases in the alignment grid are
treated as read-only.
Data Menu (in Alignment Explorer)
This menu provides commands for creating a new alignment, opening/closing
sequence data files, saving alignment sessions to a file, exporting sequence data to
a file, changing alignment sequence properties, reverse complimenting sequences
in the alignment, and exiting Alignment Explorer. The commands in this menu
are:
Create New Alignment: This tells Alignment Explorer to prepare for a new
alignment. Any sequence data currently loaded into Alignment Builder is
discarded.
Open: This submenu provides two options: opening an existing sequence
alignment session (previously saved from Alignment Explorer), and reading a text
file containing sequences in one of many formats (including, MEGA, PAUP,
FASTA, NBRF, etc.). Based on the option you choose, you will be prompted for
the file name that you wish to read.
Close: This closes the currently active data in the Alignment Explorer.
Save Session: This allows you to save the current sequence alignment to an
alignment session. You will be requested to give a file name to write the data to.
Export Alignment: This allows you to export the current sequence alignment to a
file. You can choose to export the file to MEGA or FASTA formats.
DNA Sequences: Use this item to specify that the input data is DNA. If DNA is
53
Part II: Assembling Data for Analysis
selected, then all sites are treated as nucleotides. The Translated Protein
Sequences tab contains the protein sequences. If the data is non-coding, then
ignore the second tab, as it has no affect on the on the DNA sequence tab.
However, any changes you make in the Protein Sequence tab are applied to the
DNA Sequences tab window. Note that you can UNDO these changes by using the
undo button.
Protein Sequences: Use this item to specify that the input data is amino acid
sequences. If selected, then all sites are treated as amino acid residues.
Translate/Untranslate: This item only will be available if protein-coding DNA
sequences are available in the alignment grid. It will translate protein-coding DNA
sequences into their respective amino acid sequences using the selected genetic
code table.
Select Genetic Code Table: This displays the Select Genetic Code dialog window,
which can select the genetic code table that is used when translating protein-coding
DNA sequence data.
Reverse Complement: This becomes available when an entire sequence of row(s)
is selected. It will update the selected rows to contain the reverse compliment of
the originally selected sequence(s).
Exit Alignment Explorer: This closes the Alignment Explorer window and returns
to the main MEGA application window. When selected, a message box appears
asking if you would like to save the current alignment session to a file. Then a
second message box appears asking if you would like to save the current alignment
to a MEGA file. If the current alignment is saved to a MEGA file, a third message
box will appear asking if you would like to open the saved MEGA file in the main
MEGA application.
Search Menu (in Alignment Explorer)
This menu allows searching for sequence motifs and marked sites. The commands
in this menu are:
Find Motif: This activates the Find Motif search box. When this box appears, it
asks you to enter a motif sequence (a small subsequence of a larger sequence) as
the search term. After you enter the search term, the Alignment Explorer finds each
occurrence of it and indicates it with yellow highlighting. For example, if you enter
the motif "AGA" as the search term, then each occurrence of "AGA" across all
sequences in the sequence grid would be highlighted in yellow.
Find Next: This searches for the first occurrence of the motif search term towards
the end of the current sequence. If no motif search has been performed prior to
clicking this button, the Find Motif search box will appear.
Find Previous: This searches towards the beginning of the current sequence for
the first occurrence of the motif search term. If no motif search has been performed
prior to clicking this button, the Find Motif search box will appear.
Find Marked Site: This locates the marked site in the current sequence. If no site
54
Molecular Evolutionary Genetics Analysis
has been marked for this sequence, a warning box will appear.
Highlight Motif: If this item is checked, then all occurrences of the text search
term (motif) are highlighted in the alignment grid.
Sequencer Menu (in Alignment Explorer)
Edit Sequencer File: This item displays the Open File dialog box used to open a
sequencer data file. Once opened, the sequencer data file is displayed in the Trace
Data File Viewer/Editor. This editor allows you to view and edit trace data
produced by the automated DNA sequencer. It reads and edits data in ABI and
Staden file formats and the sequences displayed can be added directly into the
Alignment Explorer or send to the Web Browser for conducting BLAST searches.
Web Menu (in Alignment Explorer)
This menu provides access to commands for querying GenBank and doing a
BLAST search, as well as access to the MEGA web Browser. The commands in
this menu are:
Query Gene Banks: This item starts the Web Browser and accesses the NCBI
home page (http://www.ncbi.nlm.nih.gov).
Do BLAST Search: This item starts the Web Browser and accesses the NCBI
BLAST query page. If you select a sequence in the alignment grid prior to
selecting this item, the web browser will automatically copy the selected sequence
data into the search field.
Show Browser: This item will show the Web Browser.
55
Part III: Input Data Types and File Format
4 Part III: Input Data Types and File Format
4.1 MEGA Input Data Formats
4.11 MEGA Format
For MEGA to read and interpret your data correctly, it should be formatted according to a
set of rules. All input data files are basic ASCII-text files, which may contain DNA
sequence, protein sequence, evolutionary distance, or phylogenetic tree data. Most word
processing packages (e.g., Microsoft Word, WordPerfect, Notepad, WordPad) allow you
to edit and save ASCII text files, which are usually marked with a .TXT extension. After
creating the file, you should change this extension to .MEG, so that you can distinguish
between your data files and the other text files. Because the organizational details vary for
different types of data, we discuss the data formats for molecular sequences, distances, and
phylogenetic trees separately. However, there are a number of features that are common to
all MEGA data files.
4.12 General Conventions
Common Features
The first line must contain the keyword #MEGA to indicate that the data file is in MEGA
format. The data file may contain a succinct description of the data (called Title) included
in the file on the second line. The Title statement is written according to a set of
rules and is copied from MEGA to every output file. In the long run, an informative title
will allow you to easily recognize your past work.
The data file may also contain a more descriptive multi-line account of the data in the
Description statement, which is written after the Title statement. The
Description statement also is written according to a set of rules. Unlike the Title
statement, the Description statement is not copied from MEGA to every output file.
In addition, the data file may also contain a Format statement, which includes
information on the type of data present in the file and some of its attributes. The Format
statement should be generally written after the Title or the Description statement.
Writing a format statement requires knowledge of the keywords used to identify different
types of data and data attributes.
All taxa names must be written according to a set of rules.
Comments can be written anywhere in the data file and can span multiple lines. They
must always be enclosed in square brackets ([and ]) brackets and can be nested.
Writing Comments
Comments can be placed anywhere in the data file as long as they are contained within a
pair of square brackets [like this]. Nested comments are allowed [[like] this].
56
Molecular Evolutionary Genetics Analysis
Key Words
MEGA supports a number of keywords, in addition to MEGA and TITLE, for writing
instructions in the format and command statements. These key words can be written in any
combination of lower- and upper-case letters. For writing instructions, follow the style
given in the examples along with the keyword description for different types of data.
Rules for Taxa Names
Distance matrices as well as sequence data may come from species, populations, or
individuals. These evolutionary entities are designated as OTUs (Operational Taxonomic
Units) or taxa. Each taxon must have an identification tag, i.e., a taxon Iabel. In the input
files prepared for use in MEGA, these labels should be written according to the following
conventions:
‘#’ Sign
Every Iabel must be written on a new line, and a '#' sign must precede the label. There are
no restrictions on the length of the Iabels in the datafile, but MEGA 4 will truncate all
labels longer than 40 characters. These labels are not required to be unique, although
identical labels may result in ambiguities and should be avoided.
Characters to use in labels
Taxa labels must start with alphanumeric characters (0-9, a-z, and A-Z) or a special
character: dash (-), plus (+) or period (.). After the first character, taxa labels may contain
the following additional special characters: underscore (_), asterisk (*), colon (:), round
open and close brackets ( ), vertical line (|),back slash (\), and forward slash (/).
For multiple word labels, an underscore can be used to represent a blank space. All
underscores are converted into blank spaces, and subsequent displays of the labels show
this change. For example, E._coli becomes E. coli.
Rules for Title Statement
A Title statement must be written on the line following the #mega. It always begins with
!Title and ends with a semicolon.
#mega
!Title
This is an example title;
A title statement may not occupy more than one line of text. It must not contain a
semicolon inside the statement, although it must contain one at the end of the statement.
Rules for Description Statement
A Description statement is written after the Title statement. It always begins with
!Description and ends with a semicolon.
#mega
!Title This is an example title;
!Description This is detailed information the data file;
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Part III: Input Data Types and File Format
A description statement may occupy multiple lines of text. It must not contain a semicolon
inside the statement, although it must contain one at the end of the statement.
Rules for the Format Statement
A format statement contains one or more command statements. A command statement
contains a command and a valid setting keyword (command=keyword format). For
example, the command statement DataType=Nucleotide tells MEGA that nucleotide
sequence data is contained in the file. Based on the DataType setting, different types of
keywords are valid.
Keywords for Sequence Data
Keywords for Distance Data
Keywords for Tree Data
4.13 Sequence Input Data
General Considerations (Sequence Data)
The sequence data must consist of two or more sequences of equal length. All sequences
must be aligned and you may use the in-built alignment system for this purpose.
Nucleotide and amino acid sequences should be written in IUPAC single-letter codes.
Sequences can be written in any combination of upper- and lower-case letters. Special
symbols for alignment gaps, missing data, and identical sites also can be included in the
sequences.
Special Symbols
Blank spaces and tabs are frequently used to format data files, so they are simply ignored
by MEGA. ASCII characters such as the period (.), dash (-), and question mark (?), are
generally used as special symbols to represent identity to the first sequence, alignment
gaps, and missing data, respectively.
IUPAC single letter codes
Nucleotide or amino acid sequences should be written in IUPAC single-letter codes. The
single-letter codes supported in MEGA are as follows.
Name
Remarks
A
Adenine
Purine
G
Guanine
Purine
C
Cytosine
Pyrimidine
Symb
ols
DNA/R
NA
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Molecular Evolutionary Genetics Analysis
T
Thymine
Pyrimidine
U
Uracil
Pyrimidine
R
Purine
A or G
Y
Pyrimindine
C or T/U
M
A or C
K
G or T
S
Strong
C or G
W
Weak
A or T
H
Not G
A or C or
T
B
Not A
C or G or
T
V
Not U/T
A or C or
G
D
Not C
A or G or
T
N
Ambiguous
A or C or
G or T
A
Alanine
Ala
C
Cysteine
Cys
D
Aspartic Acid
Asp
E
Glutamic
Acid
Glu
F
Phenylalanin
e
Phe
G
Glycine
Gly
H
Histidine
His
I
Isoleucine
Ile
K
Lysine
Lys
L
Leucine
Leu
M
Methionine
Met
Protein
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Part III: Input Data Types and File Format
N
Asparagine
Asn
P
Proline
Pro
Q
Glutamine
Gln
R
Arginine
Arg
S
Serine
Ser
T
Threonine
Thr
V
Valine
Val
W
Tryptophan
Trp
Y
Tyrosine
Tyr
*
Termination
*
Keywords for Format Statement (Sequence data)
Comm
and
Setting
Remark
Example
DataT
ype
DNA, RNA,
nucleotide,
protein
Specifies the
type of data in
the file
DataType=DNA
NSeqs
A count
Number of
sequences
NSeqs=85
NTaxa
A count
Synonymous
with NSeqs
NTaxa=85
NSites
A count
Number of
nucleotides or
amino acids
Nsites=4592
Proper
ty
Exon,
Intron,
Specifies
whether a
domain is
protein coding.
Exon and
Coding are
synonymous, as
are Intron and
Noncoding.
End specifies
that the domain
with the given
name ends at
Property=cyt_b
Coding,
Noncoding,
and End.
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Molecular Evolutionary Genetics Analysis
this point.
Indel
single
character
Use dash (-) to
identify
insertion/deletio
ns in sequence
alignments
Indel = -
Identic
al
single
character
Use period (.) to
show identify
with the first
sequence.
Identical = .
Match
Char
single
character
Synonymous
with the
identical
keyword.
MatchChar = .
Missin
g
single
character
Use a question
mark (?) to
indicate missing
data.
Missing = ?
CodeT
able
A name
This instruction
gives the name
of the code table
for the protein
coding domains
of the data
CodeTable =
Standard
Defining Genes and Domains
Writing Command Statements for Defining Genes and Domains
The MEGA format easily can designate genes and domains within the molecular sequence
data. In this format, attributes of different sites (and groups of sites, termed domains) are
specified within the data "on the spot" rather than in an attributes block before or after the
actual data, as is the case in some other data formats. An example of a three-sequence
dataset written in MEGA format is shown below. The sequences consist of three genes
named FirstGene, SecondGene, and ThirdGene for two groups of organisms Setup/Select
Genes/Domain (Mammals and Birds). (Note that the genes and domains can also be
defined interactively through a dialog box.)!Gene=FirstGene Domain=Exon1
Property=Coding;
#Human_{Mammal}
ATGGTTTCTAGTCAGGTCACCATGATAGGTCTCAAT
#Mouse_{Mammal}
ATGGTTTCTAGTCAGGTCACCATGATAGGTCCCAAT
#Chicken_{Aves}
ATGGTTTCTAGTCAGCTCACCATGATAGGTCTCAAT
!Gene=SecondGene
#Human
Domain=Intron Property=Noncoding;
ATTCCCAGGGAATTCCCGGGGGGTTTAAGGCCCCTTTAAAGAAAGAT
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Part III: Input Data Types and File Format
#Mouse
GTAGCGCGCGTCGTCAGAGCTCCCAAGGGTAGCAGTCACAGAAAGAT
#Chicken
GTAAAAAAAAAAGTCAGAGCTCCCCCCAATATATATCACAGAAAGAT
!Gene=ThirdGene
Domain=Exon2
Property=Coding;
#Human
ATCTGCTCTCGAGTACTGATACAAATGACTTCTGCGTACAACTGA
#Mouse
ATCTGATCTCGTGTGCTGGTACGAATGATTTCTGCGTTCAACTGA
#Chicken
ATCTGCTCTCGAGTACTGCTACCAATGACTTCTGCGTACAACTGA
Keywords for Command Statements (Genes/Domains)
Command
Setting
Remark
Example
Domain
A name
This instruction defines a
domain with the given name
Domain=f
irst_exon
Gene
A name
This instruction defines a gene
with the given name
Gene=cyt
b
Property
Exon,
Intron,
This instruction specifies the
protein-coding attribute for a
domain. Keywords Exon and
Coding are synonymous;
similarly Intron and
Noncoding are synonymous.
End specifies the domain in
which the given name has
ended.
Propert
y=cytb
This instruction specifies the
site where the next 1st-codon
position will be found in a
protein-coding domain.
CodonSt
art=2
Coding,
Noncoding
,
and End.
CodonStart
A number
Defining Groups
Writing Command Statements for Defining Groups of Taxa
The MEGA format allows you to assign different taxa to groups in a sequence as well as to
distance data files. In this case, the name of the group is written in a set of curly brackets
following the taxa name. The group name can be attached to the taxa name using an
underscore or just can be appended. It is important to note that there should be no spaces
62
Molecular Evolutionary Genetics Analysis
between the taxa name and group name. (Note that the groups of taxa can also be defined
interactively through a dialog box.) In the following, we show an example in which
human and mouse are designated as the members of the group Mammal and chicken
belongs to group Aves.
!Gene=FirstGene
Domain=Exon1
#Human_{Mammal}
ATGGTTTCTAGTCAGGTCACCATGATAGGTCTCAAT
#Mouse_{Mammal}
ATGGTTTCTAGTCAGGTCACCATGATAGGTCCCAAT
#Chicken_{Aves}
ATGGTTTCTAGTCAGCTCACCATGATAGGTCTCAAT
!Gene=SecondGene
Property=Coding;
Domain=Intron Property=Noncoding;
#Human
ATTCCCAGGGAATTCCCGGGGGGTTTAAGGCCCCTTTAAAGAAAGAT
#Mouse
GTAGCGCGCGTCGTCAGAGCTCCCAAGGGTAGCAGTCACAGAAAGAT
#Chicken
GTAAAAAAAAAAGTCAGAGCTCCCCCCAATATATATCACAGAAAGAT
!Gene=ThirdGene
Domain=Exon2
Property=Coding;
#Human
ATCTGCTCTCGAGTACTGATACAAATGACTTCTGCGTACAACTGA
#Mouse
ATCTGATCTCGTGTGCTGGTACGAATGATTTCTGCGTTCAACTGA
#Chicken
ATCTGCTCTCGAGTACTGCTACCAATGACTTCTGCGTACAACTGA
Setup/Select Taxa & Groups
Data | Setup/Select Taxa & Groups
This invokes the Setup/Select Taxa & Groups dialog box for including or excluding taxa,
defining groups of taxa, and editing names of taxa and groups.
Labelling Individual Sites
4.14 Site Label
The individual sites in nucleotide or amino acid data can be labeled to construct
non-contiguous sets of sites. The Setup Genes and Domains dialog can be used to
assign or edit site labels, in addition to specifying them in the input data files. This
is shown in the following example of three-sequences in which the sites in the
ThirdGene are labeled with a ‘+’ mark. An underscore marks an absence of any
labels.
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Part III: Input Data Types and File Format
!Gene=FirstGene Domain=Exon1 Property=Coding;
#Human_{Mammal} ATGGTTTCTAGTCAGGTCACCATGATAGGTCTCAAT
#Mouse_{Mammal} ATGGTTTCTAGTCAGGTCACCATGATAGGTCCCAAT
#Chicken_{Aves} ATGGTTTCTAGTCAGCTCACCATGATAGGTCTCAAT
!Gene=SecondGene Domain=AnIntron Property=Noncoding;
#Human
ATTCCCAGGGAATTCCCGGGGGGTTTAAGGCCCCTTTAAAGAAAGAT
#Mouse
GTAGCGCGCGTCGTCAGAGCTCCCAAGGGTAGCAGTCACAGAAAGAT
#Chicken
GTAAAAAAAAAAGTCAGAGCTCCCCCCAATATATATCACAGAAAGAT
!Gene=ThirdGene Domain=Exon2 Property=Coding;
#Human
ATCTGCTCTCGAGTACTGATACAAATGACTTCTGCGTACAACTGA
#Mouse
ATCTGATCTCGTGTGCTGGTACGAATGATTTCTGCGTTCAACTGA
#Chicken
ATCTGCTCTCGAGTACTGCTACCAATGACTTCTGCGTACAACTGA
!Label
+++__-+++-a-+++-L-+++-k-+++123+++-_-+++---+++;
Each site can be associated with only one label. A label can be a letter or a
number.
For analyses that require codons, MEGA includes only those codons in which all
three positions are given the same label. This site labeling system facilitates the
analysis of specific sites, as often is required for comparing sequences of
regulatory elements, intron-splice sites, and antigen recognition sites in the genes
of applications such as the Major Histocompatibility Complex.
4.15 Labeled Sites
Sites in a sequence alignment can be categorized and labeled with user-defined
symbols. Each category is represented by a letter or a number. Each site can be
assigned to only one category, although any combination of categories can be
selected for analysis.
64
Molecular Evolutionary Genetics Analysis
Labeled sites work independently of and in addition to genes and domains, thus
allowing complex subsets of sites to be defined easily.
4.16 Distance Input Data
General Considerations (Distance Data Formats)
For a set of m sequences (or taxa), there are m(m-1)/2 pairwise distances. These distances
can be arranged either in the lower-left or in the upper-right triangular matrix. After
writing the #mega,!Title,!Description, and !Format commands (some of
which are optional), you then need to write all the taxa names (see below). Taxa names
are followed by the distance matrix. An example of a matrix is:
#one
#two
#three
#four
#five
1.0 2.0
3.0
1.3
3.0
4.0
2.5
4.6
3.6
4.2
In the above example, pairwise distances are written in the upper triangular matrix (upperright format). Two alternate distance matrix formats are:
Lower-left matrix
Upper-right matrix
d1
2
d1
2
d1
3
d2
3
d1
4
d2
4
d3
4
d1
5
d2
5
d3
5
d4
5
d1
3
d1
4
d15
d2
3
d2
4
d25
d3
4
d35
d45
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Part III: Input Data Types and File Format
Keywords for Format Statement (Distance data)
Command
Setting
Remark
Example
DataType
Distance
Specifies that the
distance data is
in the file
DataType=distance
NSeqs
A count
Number of
sequences
NSeqs=85
NTaxa
A count
Same as NSeqs
NTaxa=85
DataForm
at
Lowerlef
t,
upperrig
ht
Specifies
whether the data
is in lower left
triangular matrix
or the upper right
triangular matrix
DataFormat=lowerleft
Examples below show the lower-left and the upper-right formats for a five-sequence
dataset. Note that in each case the distances are organized in a different order.
Lower-left matrix
Upper-right matrix
d1
2
d12
d1
3
d23
d1
4
d24
d34
d1
5
d25
d35
d45
d13
d14
d15
d23
d24
d25
d34
d35
d45
Defining Groups
4.17 Tree Input Data
Tree Data
* This section of the online help will be available in future updates of MEGA.
Display Newick Trees from File
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Molecular Evolutionary Genetics Analysis
Phylogeny | Display Newick Trees from File…
Use this to retrieve and display one or more trees written in Newick format. Multiple trees
can be displayed, and their consensus built, in the Tree Explorer. MEGA supports the
display of Newick format trees containing branch lengths as well as bootstrap or other
counts (note that the Newick formats do not contain the total number of bootstrap
replications conducted).
4.2 Importing Data from other Formats
4.21 Importing Data From Other Formats
MEGA 4 supports conversions from several different file formats into MEGA 4 format.
Each format is indicated by the file extension used. Supported formats include:
Extens
ion
File type
. aln
CLUSTAL
. nexus
PAUP, MacClade
. phylip
PHYLIP Interleaved
.
phylip2
PHYLIP Noninterleaved
. gcg
GCG format
. fasta
FASTA format
. pir
PIR format
. nbrf
NBRF format
. msf
MSF format
. ig
IG format
. xml
Internet (NCBI) XML format
The following sections briefly describe each of these formats and how MEGA handles
their conversion.
COMMON FILE CONVERSION ATTRIBUTES
The default input formats are determined by a file’s extension (e.g., a file with the
extension of ".ig" is initially assumed to be in "IG" input format). However, you have the
67
Part III: Input Data Types and File Format
option to specify any format for any file; the file extension is simply used as an initial
guide. Note that the specification of an incorrect file format most often results in an
erroneous conversion or other unexpected error.
Input file types can include any of the following characters in their sequence data:
•
The letters: a-z,A-Z for DNA and protein sequences
•
Peroid (.)
•
Hyphen (-)
•
The space character
•
Question mark (?).
Depending on their context, all other characters encountered in input files are either
ignored or are interpreted as specific non-sequence data, such as comments, headers, etc.
The first line of all converted files is always: #Mega
The second line of all converted file is always: !Title: <filename>
where <filename> is the name of the input file.
The third line of all converted files is blank.
Many formats can specify the length of the sequences contained within them. The MEGA
conversion utility ignores these data and does not check to see if the sequences are as long
as they are purported to be.
4.22 Convert To MEGA Format (Main File Menu)
File | Convert to MEGA Format
This item allows you to choose the file and/or the format that you would like to use to
convert a given sequence data file into a MEGA format. It converts the data file and
displays the converted data in the editor.
Files written in a number of popular data formats can be converted into MEGA format.
MEGA 4 supports conversion of CLUSTAL, NEXUS (PAUP, MacClade), PHYLIP,
GCG, FASTA, PIR, NBRF, MSF, IG, and XML formats. Details about how MEGA reads
and converts these file formats are given in the section Importing Data from Other
Formats.
4.23 Format Specific Notes
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Molecular Evolutionary Genetics Analysis
Converting CLUSTAL Format
Converting CLUSTAL Format
The sequence alignment outputs from CLUSTAL software often are given the default
extension .ALN. CLUSTAL is an interleaved format. In a page-wide arrangement the
sequence name is in the first column and a part of the sequence’s data is right justified. An
example of the CLUSTAL format follows:
CLUSTAL X (1.8) multiple sequence alignment
Q9Y2J0_Has
-----------MTDTVFSNSSNRWMYPSDRPLQSNDKEQLQAGWSVHPG
Q06846_RP3A_BOVIN
-----------MTDTVFSSSSSRWMCPSDRPLQSNDKEQLQTGWSVHPS
JX0338_rabphilin-3A-mouse ------------MTDTVVN---RWMYPGDGPLQSNDKEQLQAGWSVHPG
Q9Y2J0_Has
GQPDRQRKQEELTDEEKEIINRVIARAEKMEEMEQER-IGRLVDRLENM
Q06846_RP3A_BOVIN
GQPDRQRKQEELTDEEKEIINRVIARAEKMEEMEQER--IGRLVDRLENM
JX0338_rabphilin-3A-mouse
AQTDRQRKQEELTDEEKEIINRVIARAEKMEAMEQER--IGRLVDRLETM
The CLUSTAL file above would be converted by MEGA 4 into the following format:
#mega
Title: Bigrab2.aln
#Q9Y2J0_Hsa
------------MTDTVFSNSSNRWMYPSDRPLQSNDKEQLQAGWSVHPG
GQPDRQRKQEELTDEEKEIINRVIARAEKMEEMEQER--IGRLVDRLENM
RKNVAGDGVNRCILCGEQLGMLGSACVVCEDCKKNVCTKCGVET-NNRLH
#Q06846_RP3A_BOVIN
------------MTDTVFSSSSSRWMCPSDRPLQSNDKEQLQTGWSVHPS
GQPDRQRKQEELTDEEKEIINRVIARAEKMEEMEQER--IGRLVDRLENM
69
Part III: Input Data Types and File Format
RKNVAGDGVNRCILCGEQLGMLGSACVVCEDCKKNVCTKCGVETSNNRPH
#JX0338_rabphilin-3A-mouse
------------MTDTVVN----RWMYPGDGPLQSNDKEQLQAGWSVHPG
AQTDRQRKQEELTDEEKEIINRVIARAEKMEAMEQER--IGRLVDRLETM
RKNVAGDGVNRCILCGEQLGMLGSACVVCEDCKKNVCTKCGVETSNNRPH
Converting FASTA format
Converting FASTA format
The FASTA file format is very simple and is quite similar to the MEGA file format. This
is an example of a sample input file:
>G019uabh 400 bp
ATACATCATAACACTACTTCCTACCCATAAGCTCCTTTTAACTTGTTAAAGTCTTGCTTG
AATTAAAGACTTGTTTAAACACAAAAATTTAGAGTTTTACTCAACAAAAGTGATTGATTG
ATTGATTGATTGATTGATGGTTTACAGTAGGACTTCATTCTAGTCATTATAGCTGCTGGC
AGTATAACTGGCCAGCCTTTAATACATTGCTGCTTAGAGTCAAAGCATGTACTTAGAGTT
GGTATGATTTATCTTTTTGGTCTTCTATAGCCTCCTTCCCCATCCCCATCAGTCTTAATC
AGTCTTGTTACGTTATGACTAATCTTTGGGGATTGTGCAGAATGTTATTTTAGATAAGCA
AAACGAGCAAAATGGGGAGTTACTTATATTTCTTTAAAGC
>G028uaah 268 bp
CATAAGCTCCTTTTAACTTGTTAAAGTCTTGCTTGAATTAAAGACTTGTTTAAACACAAA
ATTTAGACTTTTACTCAACAAAAGTGATTGATTGATTGATTGATTGATTGATGGTTTACA
GTAGGACTTCATTCTAGTCATTATAGCTGCTGGCAGTATAACTGGCCAGCCTTTAATACA
TTGCTGCTTAGAGTCAAAGCATGTACTTAGAGTTGGTATGATTTATCTTTTTGGTCTTCT
ATAGCCTCCTTCCCCATCCCATCAGTCT
The MEGA file converter looks for a line that begin with a greater-than sign (‘>’), replaces
it with a pound sign (‘#’), takes the word following the pound sign as the sequence name,
deletes the rest of the line, and takes the following lines (up to the next line beginning with
70
Molecular Evolutionary Genetics Analysis
a ‘>’) as the sequence data. The MEGA file above would convert as follows:
#mega
Title: infile.fasta
#G019uabh
ATACATCATAACACTACTTCCTACCCATAAGCTCCTTTTAACTTGTTAAAGTCTTGCTTG
AATTAAAGACTTGTTTAAACACAAAAATTTAGAGTTTTACTCAACAAAAGTGATTGATTG
ATTGATTGATTGATTGATGGTTTACAGTAGGACTTCATTCTAGTCATTATAGCTGCTGGC
AGTATAACTGGCCAGCCTTTAATACATTGCTGCTTAGAGTCAAAGCATGTACTTAGAGTT
GGTATGATTTATCTTTTTGGTCTTCTATAGCCTCCTTCCCCATCCCCATCAGTCTTAATC
AGTCTTGTTACGTTATGACTAATCTTTGGGGATTGTGCAGAATGTTATTTTAGATAAGCA
AAACGAGCAAAATGGGGAGTTACTTATATTTCTTTAAAGC
#G028uaah
CATAAGCTCCTTTTAACTTGTTAAAGTCTTGCTTGAATTAAAGACTTGTTTAAACACAAA
ATTTAGACTTTTACTCAACAAAAGTGATTGATTGATTGATTGATTGATTGATGGTTTACA
GTAGGACTTCATTCTAGTCATTATAGCTGCTGGCAGTATAACTGGCCAGCCTTTAATACA
TTGCTGCTTAGAGTCAAAGCATGTACTTAGAGTTGGTATGATTTATCTTTTTGGTCTTCT
ATAGCCTCCTTCCCCATCCCATCAGTCT
Convert GCG Format
Converting GCG Format
These files consist of one or more groups of non-blank lines separated by one or more
blank lines; the non-blank lines look similar to this:
Chloroflex
Chloroflex
Check: 0 ..
Length: 428
Mon Sep 25 17:34:20 MDT 2000
1 MSKEHVQTIA TDDVSKNGHT PPTNASTPPY PFVAIVGQAE LKLALLLCVV
51 NPTIGGVMVM GHRGTAKSTA VRALAAMLPP IKAVAGCPYS CAPDRTAGLC
71
Part III: Input Data Types and File Format
101 DQCRALEQQS GKTKKPAVIN IPVPVVDLPL GATEDRVCGT LDIERALTQG
151 VQAFAPGLLA RANRGFLYID EVNLLEDHLV DVLLDVAASG VNVVEREGVS
201 VRHPARFVLV GSGNPEEGDL RPQLLDRFGL HARITTITDV SERVEIVKRR
251 REYDADPFAF VEKWAKETQK LQRKIKQAQR RLPEVILPDP VLYKIAELCV
301 KLEVDGHRGE LTLARA.ATA LAALEGRNEV TVQDVRRIAV LALRHRLRKD
351 PLETQD.... ...DAVRIER AVEEVLVP.. .......... ..........
401 .......... .......... ........
The "Check" tag near the end of a line signifies the first line in a new sequence expression.
The name of the sequence is obtained from the preceding line; the following lines, up to
the next blank line, are accepted as the sequence. For each line in the sequence, the
leading digits are stripped off, and the rest of the line is used. The following shows a
conversion of the above sequence.
#mega
Title: infile.gcg
#Chloroflex
MSKEHVQTIA TDDVSKNGHT PPTNASTPPY PFVAIVGQAE LKLALLLCVV
NPTIGGVMVM GHRGTAKSTA VRALAAMLPP IKAVAGCPYS CAPDRTAGLC
DQCRALEQQS GKTKKPAVIN IPVPVVDLPL GATEDRVCGT LDIERALTQG
VQAFAPGLLA RANRGFLYID EVNLLEDHLV DVLLDVAASG VNVVEREGVS
VRHPARFVLV GSGNPEEGDL RPQLLDRFGL HARITTITDV SERVEIVKRR
REYDADPFAF VEKWAKETQK LQRKIKQAQR RLPEVILPDP VLYKIAELCV
KLEVDGHRGE LTLARA.ATA LAALEGRNEV TVQDVRRIAV LALRHRLRKD
PLETQD.... ...DAVRIER AVEEVLVP.. .......... ..........
.......... .......... ........
Converting IG Format Files
IG Format
These files consist of one or more groups of non-blank lines separated by one or more
72
Molecular Evolutionary Genetics Analysis
blank lines. The following is an example of the non-blank lines:
;G028uaah
240 bases
G028uaah
CATAAGCTCCTTTTAACTTGTTAAAGTCTTGCTTGAATTAAAGACTTGTT
TAAACACAAAATTTAGACTTTTACTCAACAAAAGTGATTGATTGATTGAT
The first line in each group begins with a semicolon. This line is ignored by MEGA 4.
The following line (e.g., G028uaah above) is treated as the name of the sequence.
Subsequent lines, until the next semicolon, are taken as the sequence. MEGA 4 recognizes
the letters a-z and A-Z for DNA and protein sequences and only a few special characters,
such as period [.], hyphen [-], space, and question mark [?]. Depending on their context,
all other characters in the input files are either ignored or are interpreted as specific nonsequence data, such as comments, headers, etc.
The example converts to MEGA file format as follows:
#mega
!Title: filename
#G019uabh
ATACATCATAACACTACTTCCTACCCATAAGCTCCTTTTAACTTGTTAAA
GTCTTGCTTGAATTAAAGACTTGTTTAAACACAAAAATTTAGAGTTTTAC
Converting MSF Format
Converting MSF format
The MSF format is an interleaved format that is designed to simplify the comparison of
sequences with similar lengths.
G006uaah MSF: 240
2000 Check: 0 ..
Name: G019uabh
Type: N
Len: 400 Check:
Name: G028uaah
Wed Sep 20 12:57:06 MDT
0 Weight: 1.00
Len:
73
268
Check:
0
Weight:
Part III: Input Data Types and File Format
1.00
Name: G022uabh
1.00
Len:
257
Check:
0
Weight:
Name: G023uabh
1.00
Len:
347
Check:
0
Weight:
Name: G006uaah
1.00
Len:
240
Check:
0
Weight:
//
G019uabh ATACATCATA ACACTACTTC CTACCCATAA
GCTCCTTTTA ACTTGTTAAA
G028uaah CATAAGCTCC TTTTAACTTG TTAAAGTCTT
GCTTGAATTA AAGACTTGTT
G022uabh TATTTTAGAG ACCCAAGTTT TTGACCTTTT
CCATGTTTAC ATCAATCCTG
G023uabh AATAAATACC AAAAAAATAG TATATCTACA
TAGAATTTCA CATAAAATAA
G006uaah ACATAAAATA AACTGTTTTC TATGTGAAAA
TTAACCTANN ATATGCTTTG
G019uabh GTCTTGCTTG AATTAAAGAC TTGTTTAAAC
ACAAAAATTT AGAGTTTTAC
G028uaah TAAACACAAA ATTTAGACTT TTACTCAACA
AAAGTGATTG ATTGATTGAT
G022uabh TAGGTGATTG GGCAGCCATT TAAGTATTAT
TATAGACATT TTCACTATCC
G023uabh ACTGTTTTCT ATGTGAAAAT TAACCTAAAA
ATATGCTTTG CTTATGTTTA
G006uaah CTTATGTTTA AGATGTCATG CTTTTTATCA
GTTGAGGAGT TCAGCTTAAT
G019uabh TCAACAAAAG TGATTGATTG ATTGATTGAT
TGATTGATGG TTTACAGTAG
G028uaah TGATTGATTG ATGGTTTACA GTAGGACTTC
ATTCTAGTCA TTATAGCTGC
G022uabh CATTAAAACC CTTTATGCCC ATACATCATA
ACACTACTTC CTACCCATAA
G023uabh AGATGTCATG CTTTTTATCA GTTGAGGAGT
TCAGCTTAAT AATCCTCTAC
G006uaah AATCCTCTAA GATCTTAAAC AAATAGGAAA
AAAACTAAAA GTAGAAAATG
74
Molecular Evolutionary Genetics Analysis
G019uabh GACTTCATTC TAGTCATTAT AGCTGCTGGC
AGTATAACTG GCCAGCCTTT
G028uaah TGGCAGTATA ACTGGCCAGC CTTTAATACA
TTGCTGCTTA GAGTCAAAGC
G022uabh GCTCCTTTTA ACTTGTTAAA GTCTTGCTTG
AATTAAAGAC TTGTTTAAAC
G023uabh GATCTTAAAC AAATAGGAAA AAAACTAAAA
GTAGAAAATG GAAATAAAAT
G006uaah GAAATAAAAT GTCAAAGCAT TTCTACCACT
CAGAATTGAT CTTATAACAT
G019uabh AATACATTGC TGCTTAGAGT CAAAGCATGT
ACTTAGAGTT GGTATGATTT
G028uaah ATGTACTTAG AGTTGGTATG ATTTATCTTT
TTGGTCTTCT ATAGCCTCCT
G022uabh ACAAAATTTA GACTTTTACT CAACAAAAGT
GATTGATTGA TTGATTGATT
G023uabh GTCAAAGCAT TTCTACCACT CAGAATTGAT
CTTATAACAT GAAATGCTTT
G006uaah GAAATGCTTT TTAAAAGAAA ATATTAAAGT
TAAACTCCCC
G019uabh ATCTTTTTGG TCTTCTATAG CCTCCTTCCC
CATCCCCATC AGTCTTAATC
G028uaah TCCCCATCCC ATCAGTCT
G022uabh GATTGAT
G023uabh TTAAAAGAAA ATATTAAAGT TAAACTCCCC
TATTTTGCTC GTTTTTGCTT
G019uabh AGTCTTGTTA CGTTATGACT AATCTTTGGG
GATTGTGCAG AATGTTATTT
G023uabh ATCTAAAATA CATTCTGCAC AATCCCCAAA
GATTGATCAT ACGTTAC
G019uabh TAGATAAGCA AAACGAGCAA AATGGGGAGT
TACTTATATT TCTTTAAAGC
The MEGA format converter "unravels" the interleaved data by extracting each line
beginning with the first name, then those beginning with the second name, and so on,
ultimately producing a corresponding file that looks like this:
#mega
Title: thisfile.msf
75
Part III: Input Data Types and File Format
#G019uabh
ATACATCATA ACACTACTTC CTACCCATAA GCTCCTTTTA ACTTGTTAAA
GTCTTGCTTG AATTAAAGAC TTGTTTAAAC ACAAAAATTT AGAGTTTTAC
TCAACAAAAG TGATTGATTG ATTGATTGAT TGATTGATGG TTTACAGTAG
GACTTCATTC TAGTCATTAT AGCTGCTGGC AGTATAACTG GCCAGCCTTT
AATACATTGC TGCTTAGAGT CAAAGCATGT ACTTAGAGTT GGTATGATTT
ATCTTTTTGG TCTTCTATAG CCTCCTTCCC CATCCCCATC AGTCTTAATC
AGTCTTGTTA CGTTATGACT AATCTTTGGG GATTGTGCAG AATGTTATTT
TAGATAAGCA AAACGAGCAA AATGGGGAGT TACTTATATT TCTTTAAAGC
#G028uaah
CATAAGCTCC TTTTAACTTG TTAAAGTCTT GCTTGAATTA AAGACTTGTT
TAAACACAAA ATTTAGACTT TTACTCAACA AAAGTGATTG ATTGATTGAT
TGATTGATTG ATGGTTTACA GTAGGACTTC ATTCTAGTCA TTATAGCTGC
TGGCAGTATA ACTGGCCAGC CTTTAATACA TTGCTGCTTA GAGTCAAAGC
ATGTACTTAG AGTTGGTATG ATTTATCTTT TTGGTCTTCT ATAGCCTCCT
TCCCCATCCC ATCAGTCT
#G022uabh
TATTTTAGAG ACCCAAGTTT TTGACCTTTT CCATGTTTAC ATCAATCCTG
TAGGTGATTG GGCAGCCATT TAAGTATTAT TATAGACATT TTCACTATCC
CATTAAAACC CTTTATGCCC ATACATCATA ACACTACTTC CTACCCATAA
GCTCCTTTTA ACTTGTTAAA GTCTTGCTTG AATTAAAGAC TTGTTTAAAC
ACAAAATTTA GACTTTTACT CAACAAAAGT GATTGATTGA TTGATTGATT
GATTGAT
#G023uabh
AATAAATACC AAAAAAATAG TATATCTACA TAGAATTTCA CATAAAATAA
ACTGTTTTCT ATGTGAAAAT TAACCTAAAA ATATGCTTTG CTTATGTTTA
AGATGTCATG CTTTTTATCA GTTGAGGAGT TCAGCTTAAT AATCCTCTAC
76
Molecular Evolutionary Genetics Analysis
GATCTTAAAC AAATAGGAAA AAAACTAAAA GTAGAAAATG GAAATAAAAT
GTCAAAGCAT TTCTACCACT CAGAATTGAT CTTATAACAT GAAATGCTTT
TTAAAAGAAA ATATTAAAGT TAAACTCCCC TATTTTGCTC GTTTTTGCTT
ATCTAAAATA CATTCTGCAC AATCCCCAAA GATTGATCAT ACGTTAC
#G006uaah
ACATAAAATA AACTGTTTTC TATGTGAAAA TTAACCTANN ATATGCTTTG
CTTATGTTTA AGATGTCATG CTTTTTATCA GTTGAGGAGT TCAGCTTAAT
AATCCTCTAA GATCTTAAAC AAATAGGAAA AAAACTAAAA GTAGAAAATG
GAAATAAAAT GTCAAAGCAT TTCTACCACT CAGAATTGAT CTTATAACAT
GAAATGCTTT TTAAAAGAAA ATATTAAAGT TAAACTCCCC
Converting NBRF Format
Converting NBRF Format
NBRF files consist of one or more groups of non-blank lines separated by one or more
blank lines; the non-blank lines look similar to this:
>P1;Chloroflex
Chloroflex
428 bases
MSKEHVQTIA TDDVSKNGHT PPTNASTPPY PFVAIVGQAE LKLALLLCVV
NPTIGGVMVM GHRGTAKSTA VRALAAMLPP IKAVAGCPYS CAPDRTAGLC
DQCRALEQQS GKTKKPAVIN IPVPVVDLPL GATEDRVCGT LDIERALTQG
VQAFAPGLLA RANRGFLYID EVNLLEDHLV DVLLDVAASG VNVVEREGVS
VRHPARFVLV GSGNPEEGDL RPQLLDRFGL HARITTITDV SERVEIVKRR
REYDADPFAF VEKWAKETQK LQRKIKQAQR RLPEVILPDP VLYKIAELCV
KLEVDGHRGE LTLARA-ATA LAALEGRNEV TVQDVRRIAV LALRHRLRKD
PLETQD---- ---DAVRIER AVEEVLVP-- ---------- ------------------- ---------- --------*
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Part III: Input Data Types and File Format
Each group begins with a line starting with a greater-than symbol (‘>’). This line is
ignored. The first word in the following line (e.g., Chloroflex above) is treated as the
name of the sequence; the rest of that line is ignored Subsequent lines are taken as the
sequence. This example would be converted to the MEGA file format as follows:
#mega
!Title: filename
#Chloroflex
MSKEHVQTIA TDDVSKNGHT PPTNASTPPY PFVAIVGQAE LKLALLLCVV
NPTIGGVMVM GHRGTAKSTA VRALAAMLPP IKAVAGCPYS CAPDRTAGLC
DQCRALEQQS GKTKKPAVIN IPVPVVDLPL GATEDRVCGT LDIERALTQG
VQAFAPGLLA RANRGFLYID EVNLLEDHLV DVLLDVAASG VNVVEREGVS
VRHPARFVLV GSGNPEEGDL RPQLLDRFGL HARITTITDV SERVEIVKRR
REYDADPFAF VEKWAKETQK LQRKIKQAQR RLPEVILPDP VLYKIAELCV
KLEVDGHRGE LTLARA-ATA LAALEGRNEV TVQDVRRIAV LALRHRLRKD
PLETQD---- ---DAVRIER AVEEVLVP-- ---------- ------------------- ---------- --------
Converting Nexus Format
Format: nexus
The NEXUS file format has a header with lines identifying the name of each of the
sequences in the file, followed by lines that begin with the sequence name and some data.
An example of part of an input file is:
#NEXUS
BEGIN DATA;
DIMENSIONS NTAX=17 NCHAR=428;
FORMAT DATATYPE=PROTEIN INTERLEAVE MISSING=-;
[Name: Chloroflex
Len:
428
Check:
0]
[Name: Rcapsulatu
Len:
428
Check:
0]
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Molecular Evolutionary Genetics Analysis
MATRIX
Chloroflex MSKEHVQTIATDDVSKNGHT
PPTNASTPPYPFVAIVGQAE
Rcapsulatu ---------MTTAVARLQPS
ASGAKTRPVFPFSAIVGQED
Chloroflex DQCRALEQQSGKTKKPAVIN
IPVPVVDLPLGATEDRVCGT
Rcapsulatu DWATVLS-----TN---VIR
KPTPVVDLPLGVSEDRVVGA
The MEGA 4 conversion function looks for all the lines starting with the "[Name:" flag
and takes the following word as a sequence name. The conversion function then scans
through the data looking for all lines starting with each of the identified names and places
them on the output. This appears as follows:
#mega
Title: infile.nexus
#Chloroflex
MSKEHVQTIATDDVSKNGHT PPTNASTPPYPFVAIVGQAE
DQCRALEQQSGKTKKPAVIN IPVPVVDLPLGATEDRVCGT
#Rcapsulatu
---------MTTAVARLQPS ASGAKTRPVFPFSAIVGQED
DWATVLS-----TN---VIR KPTPVVDLPLGVSEDRVVGA
Converting PHYLIP (interleaved) Format
Converting the PHYLIP interleaved file format
The PHYLIP format is interleaved, similar to the MSF format. It consists of a line of
numeric data, which is ignored by MEGA 4, followed by a group of one or more lines of
text. The text begins with a sequence name in the first column and is followed by the
initial part of each sequence; the group is terminated by a blank line. The number of lines
in subsequent groups of data is similar to the first group. Each line is a continuation of the
identified sequence and begins in the same position as in the first group. The following
might be observed at the beginning of a PHYLIP data file:
79
Part III: Input Data Types and File Format
2 2000 I
G019uabh
ACTTGTTAAA
ATACATCATA ACACTACTTC CTACCCATAA GCTCCTTTTA
G028uaah
AAGACTTGTT
CATAAGCTCC TTTTAACTTG TTAAAGTCTT GCTTGAATTA
GTCTTGCTTG AATTAAAGAC TTGTTTAAAC ACAAAAATTT
AGAGTTTTAC
TAAACACAAA ATTTAGACTT TTACTCAACA AAAGTGATTG
ATTGATTGAT
TCAACAAAAG TGATTGATTG ATTGATTGAT TGATTGATGG
TTTACAGTAG
TGATTGATTG ATGGTTTACA GTAGGACTTC ATTCTAGTCA
TTATAGCTGC
MEGA 4 would convert this data as follows:
#mega
Title: cap-data.phylip
#G019uabh
ATACATCATA ACACTACTTC CTACCCATAA GCTCCTTTTA ACTTGTTAAA
GTCTTGCTTG AATTAAAGAC TTGTTTAAAC ACAAAAATTT AGAGTTTTAC
TCAACAAAAG TGATTGATTG ATTGATTGAT TGATTGATGG TTTACAGTAG
#G028uaah
CATAAGCTCC TTTTAACTTG TTAAAGTCTT GCTTGAATTA AAGACTTGTT
TAAACACAAA ATTTAGACTT TTACTCAACA AAAGTGATTG ATTGATTGAT
TGATTGATTG ATGGTTTACA GTAGGACTTC ATTCTAGTCA TTATAGCTGC
80
Molecular Evolutionary Genetics Analysis
Converting PHYLIP (Noninterleaved) Format
Converting PHYLIP non-interleaved format
While otherwise similar to the PHYLIP interleaved format, this format is not interleaved.
For example:
0 0 I
G019uabh
ACTTGTTAAA
ATACATCATA ACACTACTTC CTACCCATAA GCTCCTTTTA
GTCTTGCTTG AATTAAAGAC TTGTTTAAAC ACAAAAATTT
AGAGTTTTAC
TCAACAAAAG TGATTGATTG ATTGATTGAT TGATTGATGG
TTTACAGTAG
GACTTCATTC TAGTCATTAT AGCTGCTGGC AGTATAACTG
GCCAGCCTTT
AATACATTGC TGCTTAGAGT CAAAGCATGT ACTTAGAGTT
G028uaah
AAGACTTGTT
CATAAGCTCC TTTTAACTTG TTAAAGTCTT GCTTGAATTA
TAAACACAAA ATTTAGACTT TTACTCAACA AAAGTGATTG
ATTGATTGAT
TGATTGATTG ATGGTTTACA GTAGGACTTC ATTCTAGTCA
TTATAGCTGC
TGGCAGTATA ACTGGCCAGC CTTTAATACA TTGCTGCTTA
GAGTCAAAGC
ATGTACTTAG AGTTGGTATG ATTTATCTTT TTGGTCTTCT
This file would be converted to MEGA format as follows:
#mega
Title: infile.phylip2
#G019uabh
ATACATCATA ACACTACTTC CTACCCATAA GCTCCTTTTA ACTTGTTAAA
GTCTTGCTTG AATTAAAGAC TTGTTTAAAC ACAAAAATTT AGAGTTTTAC
TCAACAAAAG TGATTGATTG ATTGATTGAT TGATTGATGG TTTACAGTAG
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Part III: Input Data Types and File Format
GACTTCATTC TAGTCATTAT AGCTGCTGGC AGTATAACTG GCCAGCCTTT
AATACATTGC TGCTTAGAGT CAAAGCATGT ACTTAGAGTT
#G028uaah
CATAAGCTCC TTTTAACTTG TTAAAGTCTT GCTTGAATTA AAGACTTGTT
TAAACACAAA ATTTAGACTT TTACTCAACA AAAGTGATTG ATTGATTGAT
TGATTGATTG ATGGTTTACA GTAGGACTTC ATTCTAGTCA TTATAGCTGC
TGGCAGTATA ACTGGCCAGC CTTTAATACA TTGCTGCTTA GAGTCAAAGC
ATGTACTTAG AGTTGGTATG ATTTATCTTT TTGGTCTTCT
Converting PIR Format
Converting PIR Format
These files consist of groups of non-blank lines that look similar to this:
ENTRY
G006uaah
TITLE
G019uabh 400 bp 240 bases
SEQUENCE
5
10
15
20
25
30
1 A C A T A A A A T A A A C T G T T T T C T A T G T G
A A A A
31 T T A A C C T A N N A T A T G C T T T G C T T A T G
T T T A
61 A G A T G T C A T G C T T T T T A T C A G T T G A G
G A G T
91 T C A G C T T A A T A A T C C T C T A A G A T C T T
A A A C
121 A A A T A G G A A A A A A A C T A A A A G T A G A A
A A T G
151 G A A A T A A A A T G T C A A A G C A T T T C T A C
C A C T
181 C A G A A T T G A T C T T A T A A C A T G A A A T G
C T T T
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Molecular Evolutionary Genetics Analysis
211 T T A A A A G A A A A T A T T A A A G T T A A A C T
C C C C
The MEGA format converter looks for the "ENTRY" tag and treats the following string as
the sequence name, e.g., G006uaah above. The remaining lines have their digits and
spaces removed; any non-sequence characters also are deleted. MEGA would convert the
above sequence as follows:
#mega
Title: filename.pir
#G006uaah
ACATAAAATAAACTGTTTTCTATGTGAAAA
TTAACCTANNATATGCTTTGCTTATGTTTA
AGATGTCATGCTTTTTATCAGTTGAGGAGT
TCAGCTTAATAATCCTCTAAGATCTTAAAC
AAATAGGAAAAAAACTAAAAGTAGAAAATG
GAAATAAAATGTCAAAGCATTTCTACCACT
CAGAATTGATCTTATAACATGAAATGCTTT
TTAAAAGAAAATATTAAAGTTAAACTCCCC
Converting XML format
Converting XML Format
These files consist of a group of XML tags and attribute values. A DOCTYPE header may
or may not be present. This is a relatively new format and is subject to revision. The
MEGA input converter for XML file formats does not implement a full parser; it only
looks for a few specific tags that might be present. For example, an XML file might
contain the following data:
<Bioseq-set>
<Bioseq>
<name>G019uabh</name>
<length>240</length>
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Part III: Input Data Types and File Format
<mol>DNA</mol>
<cksum>302C447C</cksum>
<seqdata>ATACATCATAACACTACTTCCTACCCATAAGCTCCTTTTAACTTGTTAAAGTCTT
GCTTGAATT
AAAGACTTGTTTAAACACAAAAATTTAGAGTTTTACTCAACAAAAGTGATTGATTGATTG
ATTGATTGATTGATGGTT
TACAGTAGGACTTCATTCTAGTCATTATAGCTGCTGGCAGTATAACTGGCCAGCCTTTAA
TACATTGCTGCTTAGAGT
CAAAGCATGTACTTAGAGTT</seq-data>
</Bioseq>
</Bioseq-set>
The MEGA format converter looks for the following two tags:
<name>G019uabh</name>
<seq-data>ATACATCATAACACTAC. . .</seq-data>
If it finds these tags, it uses the text between the <name>. . .</name> tags as the
sequence name, and the text between the <seq-data>. . .</seq-data> tags as
the sequence data corresponding to that name. The conversion of the above XML block
into MEGA format would look like this:
#Mega
Title: filename.xml
#G019uabh
ATACATCATAACACTACTTCCTACCCATAAGCTCCTTTTAACTTGTTAAAGTCTTGCTTG
AATT
AAAGACTTGTTTAAACACAAAAATTTAGAGTTTTACTCAACAAAAGTGATTGATTGATTG
ATTGATTGATTGATGGTT
TACAGTAGGACTTCATTCTAGTCATTATAGCTGCTGGCAGTATAACTGGCCAGCCTTTAA
TACATTGCTGCTTAGAGT
4.3 Genetic Code Tables
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Molecular Evolutionary Genetics Analysis
4.31 Built-in Genetic Codes
MEGA 4 contains four commonly used genetic code tables: (1) Standard, (2) Vertebrate
mitochondrial, (3) Drosophila mitochondrial, and (4) Yeast mitochondrial. They can be
used as templates to create additional genetic code tables using the Genetic Code Selector.
Genetic codes for these four built-in tables in one letter code are given below.
Code Table
Code Table
Codon
1
2
3
4
Codon
1
2
3
4
UUU
F
F
F
F
AUU
I
I
I
I
UUC
F
F
F
F
AUC
I
I
I
I
UUA
L
L
L
L
AUA
I
M
M
I
UUG
L
L
L
L
AUG
M
M
M
M
UCU
S
S
S
S
ACU
T
T
T
T
UCC
S
S
S
S
ACC
T
T
T
T
UCA
S
S
S
S
ACA
T
T
T
T
UCG
S
S
S
S
ACG
T
T
T
T
UAU
Y
Y
Y
Y
AAU
N
N
N
N
UAC
Y
Y
Y
Y
AAC
N
N
N
N
UAA
*
*
*
*
AAA
K
K
K
K
UAG
*
*
*
*
AAG
K
K
K
K
UGU
C
C
C
C
AGU
S
S
S
S
UGC
C
C
C
C
AGC
S
S
S
S
UGA
*
W
W
W
AGA
R
*
S
R
UGG
W
W
W
W
AGG
R
*
S
R
CUU
L
L
L
T
GUU
V
V
V
V
CUC
L
L
L
T
GUC
V
V
V
V
CUA
L
L
L
T
GUA
V
V
V
V
CUG
L
L
L
T
GUG
V
V
V
V
CCU
P
P
P
P
GCU
A
A
A
A
CCC
P
P
P
P
GCC
A
A
A
A
CCA
P
P
P
P
GCA
A
A
A
A
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Part III: Input Data Types and File Format
CCG
P
P
P
P
GCG
A
A
A
A
CAU
H
H
H
H
GAU
D
D
D
D
CAC
H
H
H
H
GAC
D
D
D
D
CAA
Q
Q
Q
Q
GAA
E
E
E
E
CAG
Q
Q
Q
Q
GAG
E
E
E
E
CGU
R
R
R
R
GGU
G
G
G
G
CGC
R
R
R
R
GGC
G
G
G
G
CGA
R
R
R
R
GGA
G
G
G
G
CGG
R
R
R
R
GGG
G
G
G
G
4.32 Adding/Modifying Genetic Code Tables
You may add new genetic code tables and/or edit existing code tables in the Genetic Code
Selector. All changes made will be remembered by MEGA for all future analyses.
4.33 Computing Statistical Attributes (Genetic Code)
There is a significant amount of redundancy in the genetic code because most amino acids
are encoded by multiple codons. Therefore, it is interesting to know the degeneracy of
each codon position in all codons. In MEGA 4 this information can be computed for any
code table in the Genetic Code Selector. In addition to the degeneracy of the codon
positions, MEGA 4 writes the number of synonymous sites and the number of
nonsynonymous sites for each codon using the Nei and Gojobori (1986) method. An
example of the results obtained for the standard genetic code is given below:
Code Table: Standard
Method: Nei-Gojobori (1986)
methodology
S = No. of synonymous
sites
N = No. of nonsynonymous sites
Codo
n
No. of
Sites for
codon
S
N
Redundancy
P
os
1s
t
P
os
2
n
Pos
3rd
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Molecular Evolutionary Genetics Analysis
d
UUU
(F)
0.3
33
2.6
67
0
0
2
UUC
(F)
0.3
33
2.6
67
0
0
2
UUA
(L)
0.6
67
2.3
33
2
0
2
UUG
(L)
0.6
67
2.3
33
2
0
2
UCU
(S)
1
2
0
0
4
UCC
(S)
1
2
0
0
4
UCA
(S)
1
2
0
0
4
UCG
(S)
1
2
0
0
4
UAU
(Y)
1
2
0
0
2
UAC
(Y)
1
2
0
0
2
UAA
(*)
0
3
0
0
0
UAG
(*)
0
3
0
0
0
UGU
(C)
0.5
2.5
0
0
2
UGC
(C)
0.5
2.5
0
0
2
UGA
(*)
0
3
0
0
0
UGG
(W)
0
3
0
0
0
CUU
(L)
1
2
0
0
4
CUC
(L)
1
2
0
0
4
CUA
(L)
1.3
33
1.6
67
2
0
4
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Part III: Input Data Types and File Format
Select Genetic Code Table
Data | Select Genetic Code Table
Use the Select Genetic Code Table dialog from the Data menu to select the genetic code
used by the protein-coding nucleotide sequence data. This also allows you to add genetic
codes to the list, edit existing codes, and compute a few simple statistical properties of the
chosen genetic code. This option becomes visible when you open a data set containing
nucleotide sequences.
4.34 Code Table Editor
The Code Table Editor allows you to create new genetic codes and to edit existing genetic
codes. It contains the code of the highlighted genetic code table from the previous
window. To name the new genetic code or to change an existing code, click in the 'Name'
box and type the new name.
The genetic code in this editor is set up intuitively. To save space, only the amino acid
encoded by a codon is shown. The first position of the codon is shown on the left, the
second position on the top, and the third position on the right. To find the codon for any
given entry on the screen, position your mouse over the desired amino acid and wait for a
moment; a yellow hint will be displayed.
To change the amino acid encoded by any codon, click and scroll down to choose the
desired amino acid. Alternatively, once the codon has been selected, type in the first letter
of the name of the amino acid and the program will jump to that part of the list. To indicate
a stop codon, select '***' or type *.
Once you have made all the required changes to the name and codons, click OK.
Otherwise, click Cancel. We recommend that you check the altered genetic code using the
View option to make sure that the changes have been properly interpreted by MEGA.
4.4 Viewing and Exploring Input Data
4.41 Sequence Data Explorer
4.42 Sequence Data Explorer
The Sequence Data Explorer shows the aligned sequence data. You can scroll
along the alignment using the scrollbar at the bottom right hand side of the explorer
window. The Sequence Data Explorer provides a number of useful functionalities
for exploring the statistical attributes of the data and also for selecting data subsets.
88
Molecular Evolutionary Genetics Analysis
This explorer consists of a number of regions as follows:
Menu Bar
Data menu
Display menu
Highlight menu
Statistics menu
Help: This item brings up the help file for the Sequence Data Explorer.
Tool Bar
The tool bar provides quick access to the following menu items:
General Utilities
•
•
: This brings up the Exporting Sequence Data dialog box, which
contains options to control how MEGA writes the output data.
•
Color: This brings up a color palette selection box with which you can
choose the color to be displayed in the highlighted sites.
•
: This brings up the dialog box for setting up and selecting domains
and genes.
•
: This brings up the dialog box for setting up, editing, and selecting
taxa and groups of taxa.
•
: This toggle replaces the nucleotide (amino acid) at a site with the
identical symbol (e.g. a dot) if the site contains the same nucleotide (amino
acid).
•
Highlighting Sites
•
C: If this button is pressed, then all constant sites will be highlighted. A
count of the highlighted sites will be displayed on the status bar.
•
V: If this button is pressed, then all variable sites will be highlighted. A
count of the highlighted sites will be displayed on the status bar.
•
Pi: If this button is pressed, then all parsimony-informative sites will be
highlighted. A count of the highlighted sites will be displayed on the
status bar.
•
S: If this button is pressed, then all singleton sites will be highlighted. A
count of the highlighted sites will be displayed on the status bar.
•
0: If this button is pressed, then sites will be highlighted only if they are
zero-fold degenerate sites in all sequences displayed. A count of
highlighted sites will be displayed on the status bar. (This button is
89
Part III: Input Data Types and File Format
available only if the dataset contains protein coding DNA sequences).
•
•
2: If this button is pressed, then sites will be highlighted only if they are
two-fold degenerate sites in all sequences displayed. A count of
highlighted sites will be displayed on the status bar. (This button is
available only if the dataset contains protein coding DNA sequences).
•
4: If this button is pressed, then sites will be highlighted only if they are
four-fold degenerate sites in all sequences displayed. A count of
highlighted sites will be displayed on the status bar. (This button is
available only if the dataset contains protein coding DNA sequences).
: This button provides the facility to translate
codons in the sequence data into amino acid sequences and back. All
protein-coding regions will be automatically identified and translated for
display. When the translated sequence is already displayed, then issuing
this command displays the original nucleotide sequences (including all
coding and non-coding regions). Depending on the data displayed
(translated or nucleotide), relevant menu options in the Sequence Data
Explorer become enabled. Note that the translated/un-translated status in
this data explorer does not have any impact on the options for analysis
available in MEGA (e.g., Distances or Phylogeny menus), as MEGA
provides all possible options for your dataset at all times.
The 2-Dimensional Data Grid
Fixed Row: This is the first row in the data grid. It is used to display the
nucleotides (or amino acids) in the first sequence when you have chosen to
show their identity using a special character. For protein coding regions, it
also clearly marks the first, second, and the third codon positions.
Fixed Column: This is the first and the leftmost column in the data grid. It is
always visible, even when you are scrolling through sites. The column
contains the sequence names and an associated check box. You can check or
uncheck this box to include or exclude a sequence from analysis. Also in this
column, you can drag-and-drop sequences to sort them.
Rest of the Grid: Cells to the right of and below the first row contain the
nucleotides or amino acids of the input data. Note that all cells are drawn in
light color if they contain data corresponding to unselected sequences or
genes or domains.
Status Bar
This section displays the location of the focused site and the total sequence
length. It also shows the site label, if any, and a count of the highlighted
sites.
Data Menu
Data Menu
90
Molecular Evolutionary Genetics Analysis
This allows you to explore the active data set, and establish various data attributes, and data
subset options.
Data Menu (in Sequence Data Explorer)
This menu provides commands for working with selected data in the Sequence
Data Explorer
The commands in this menu are:
Write Data to File
Brings up the Exporting
Sequence Data dialog box.
Translate/Untranslat
e
Translates protein-coding
nucleotide sequences into
protein sequences, and back to
nucleotide sequences.
Select Genetic Code
Table
Brings up the Select Genetic
Code dialog box, in which you
can select, edit or add a genetic
code table.
Setup/Select Genes
and Domains
Brings up the Sequence Data
Organizer, in which you can
define and edit genes and
domains.
Setup/Select Taxa
and Groups
Brings up the Select/Edit Taxa
and Groups dialog, in which you
can edit taxa and define groups
of taxa.
Quit Data Viewer
Takes the user back to the main
interface.
Translate/Untranslate (in Sequence Data Explorer)
Data | Translate/Untranslate
This command is available only if the data contain protein-coding nucleotide
sequences. It automatically extracts all protein-coding domains for translation and
displays the corresponding protein sequence. If the translated sequence is already
displayed, then issuing this command displays the original nucleotide sequences,
including all coding and non-coding regions. Depending on the data displayed
(translated or nucleotide), relevant menu options in the Sequence Data Explorer are
enabled. However, translated and un-translated status does not have any impact on
the analytical options available in MEGA (e.g., Distances or Phylogeny menus), as
MEGA provides all possible options for your dataset at all times.
Select Genetic Code Table (in Sequence Data Explorer)
Data | Select Genetic Code Table
Select Genetic Code Table, can be invoked from within the Data menu in Sequence
Data Explorer, and is also available in the main interface directly in the Data
91
Part III: Input Data Types and File Format
Menu.
Setup/Select Taxa & Groups (in Sequence Data Explorer)
Data | Setup/Select Taxa & Groups
Setup/Select Taxa & Groups, can be invoked from within the Data menu in
Sequence Data Explorer, and is also available in the main interface directly in the
Data Menu.
Setup/Select Genes & Domains (in Sequence Data Explorer)
Data | Setup/Select Genes & Domains
Setup/Select Genes & Domains, can be invoked from within the Data menu in
Sequence Data Explorer, and is also available in the main interface directly in the
Data Menu.
Export Data (in Sequence Data Explorer)
Data | Export Data
The Exporting Sequence Data dialog box first displays an edit box for entering a
title for the sequence data being exported. The default name is the original name
of the data set, if there was one. Below the title is a space for entering a brief
description of the data set being exported.
Next is the option for determining the format of the data set being exported; MEGA
currently allows the user to export the data in MEGA, PAUP 3.0 and PAUP 4.0
(Nexus, Interleaved in both cases), and PHYLIP 3.0 (Interleaved). tA the end of
each line, is "Writing site numbers." The three options available are to not write
any number, to write one for each site, or to write the site number of the last site.
Other options in this dialog box include the number of sites per line, which codon
position(s) is to be used and whether noncoding regions should be included, and
whether the output is to be interleaved. For missing or ambiguous data and
alignment gaps, there are four options: include all such data, exclude all such data,
exclude or include sites with missing or ambiguous data only, and exclude sites
with alignment gaps only.
Quit Data Viewer
Data | Quit Data Viewer
This command closes the Sequence Data Explorer, and takes the user back to main
interface.
92
Molecular Evolutionary Genetics Analysis
Display Menu
Display Menu (in Sequence Data Explorer)
This menu provides commands for adjusting the display of DNA and protein sequences in the grid.
The commands in this menu are:
•
Show only selected sequences: To work only in a subset of the sequences in the
data set, use the check boxes to select the sequences of interest.
•
Use Identical Symbol: If this site contains the same nucleotide (amino acid) as
appears in the first sequence in the list, this command replaces the nucleotide
(amino acid) symbol with a dot (.). If you uncheck this option, the Sequence
Data Explorer displays the single letter code for the nucleotide (amino acid).
•
Color Cells: This option displays the sequences such that consecutive sites
with the same nucleotide (amino acid) have the same background color.
•
Sort Sequences: The sequences in the data set can be sorted based on several
options: sequence names, group names, group and sequence names, or as per
the order in the Select/Edit Taxa Groups dialog box.
•
Restore input order: This option resets any changes in the order of the
displayed sequences (due to sorting, etc.) back to that in the input data file.
•
Show Sequence Name: The name of the sequences can be displayed or hidden
by checking or unchecking this option. If the sequences have been grouped,
then unchecking this option causes only the group name to be retained. If no
groups have been made, then no name is displayed.
•
Show Group Name. This option can be used to display or hide group names if
the taxa have been categorized into groups.
•
Change Font. Brings up the Font dialog box, allowing the user to choose the
type, style, size, etc. of the font to display the sequences.
Restore Input Order
Display | Restore Input Order
Choosing this restores the order in Sequence Data Explorer to that in the input text
file.
Show Only Selected Sequences
Display | Show only Selected Sequences
The check boxes in the left column of the display grid can be used to select or
deselect sequences for analysis. Subsequent use of the "Show Only Selected
Sequences" option in the Display menu of Sequence Data Explorer hides all the
deselected sequences and displays only the selected ones.
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Part III: Input Data Types and File Format
Color Cells
Display | Color cells
This command colors individual cells in the two-dimensional display grid
according to the nucleotide or amino acid it contains. A list of default colors,
based on the biochemical properties of the residues, is given below. In a future
version, these colors will be customizable by the user.
For DNA sequences:
Sym
bol
Colo
r
A
Yello
w
G
Fuchs
ia
C
Olive
T
Gree
n
U
Gree
n
For amino acid sequences:
Sym
bol
Colo
r
Sym
bol
Colo
r
A
Yell
ow
M
Yello
w
C
Oliv
e
N
Gree
n
D
Aqu
a
P
Blue
E
Aqu
a
Q
Gree
n
F
Yell
ow
R
Red
G
Fuch
sia
S
Gree
n
H
Teal
T
Gree
n
I
Yell
ow
V
Yello
w
94
Molecular Evolutionary Genetics Analysis
K
Red
W
Gree
n
L
Yell
ow
Y
Lime
Use Identical Symbol
Display | Use Identical Symbol
Data that contain multiple aligned sequences may be easier to view if, when the
nucleotide (amino acid) is the same as that in the corresponding site in the first
sequence, the nucleotide (amino acid) is replaced by a dot. Choosing this option
again brings back the nucleotide (amino acid) single-letter codes.
Show Sequence Names
Display | Show Sequence Names
This option displays the full sequence names in Sequence Data Explorer
Show Group Names
Display | Show Group Names
This option displays the full group names in Sequence Data Explorer if the
sequences have been grouped in Select/Edit Taxa Groups
Change Font...
Display | Change Font…
This command brings up the Change Font dialog box, which allows you to change
the display font, including font type, style and size. Options to strikeout or
underline selected parts of the sequences are also available. There is also an option
for using different scripts, although the only option currently available is
"Western". Finally the "Sample" window displays the effects of your choices
Sort Sequences
Display | Sort Sequences
The sequences in the data set can be sorted based on several options: sequence
name, group name, group and sequence names, or as per the order in the
Select/Edit Taxa Groups dialog box.
95
Part III: Input Data Types and File Format
Sort Sequences by Group Name
Display | Sort Sequences | By Group Name
Sequences that have been grouped in Select/Edit Taxa Groups can be sorted by the
alphabetical order of group names or numerical order of group ID numbers. If the
group names contain both a name and a number, the numerical order will be nested
within the alphabetical order.
Sort Sequences by Group and Sequence Names
Display | Sort Sequences | By Group and Sequence Names
Sequences that have been grouped in Select/Edit Taxa Groups can be sorted by the
alphabetical order of group names or the numerical order of group ID numbers. If
the group names contain both a name and a number, the numerical order is nested
within the alphabetical order. The sequences can be further arranged by sorting the
sequence names within the group names.
Sort Sequences As per Taxa/Group Organizer
Display | Sort Sequences | As per Taxa/Group Organizer
The sequence/group order seen in Select/Edit Taxa Groups is initially the same as
the order in the input text file. However, this order can be changed by draggingand-dropping. Choose this option if you wish to see the data in the same order in
the Sequence Data Explorer as in Select/Edit Taxa Groups.
Sort Sequences By Sequence Name
Display | Sort Sequences | By Sequence Name
The sequences are sorted by the alphabetical order of sequence names or the
numerical order of sequence ID numbers. If the sequence names contain both a
name and a number, then the sorting is done with the numerical order nested within
the alphabetical order.
Highlight Menu
Highlight Menu (in Sequence Data Explorer)
This menu can be used to highlight certain types of sites. The options are constant
sites, variable sites, parsimony-informative sites, singleton sites, 0-fold, 2-fold and
4-fold degenerate sites.
Highlight Conserved Sites
Highlight | Conserved Sites
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Use this command to highlight constant sites
Highlight Variable Sites
Highlight | Variable Sites
Use this command to highlight variable sites sites.
Highlight Singleton Sites
Highlight | Singleton Sites
Use this command to highlight singleton sites.
Highlight Parsimony Informative Sites
Highlight | Parsim-Info Sites
Use this command to highlight parsimony-informative sites.
Highlight 0-fold Degenerate Sites
Highlight | 0-fold Degenerate Sites
Use this command to highlight 0-fold degenerate sites.
Highlight 2-fold Degenerate Sites
Highlight | 2-fold Degenerate Sites
Use this command to highlight 2-fold degenerate sites. The command is visible
only if the data consists of nucleotide sequences.
Highlight 4-fold Degenerate Sites
Highlight | 4-fold Degenerate Sites
Use this command to highlight 4-fold degenerate sites. The command is visible
only if the data consists of nucleotide sequences.
Statistics Menu
Statistics Menu (in Sequence Data Explorer)
Various summary statistics of the sequences can be computed and displayed using
this menu. The commands are:
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Nucleotide Composition.
Nucleotide Pair Frequencies.
Codon Usage.
Amino Acid Composition.
Use All Selected Sites.
Use only Highlighted Sites. Sites can be selected according to various criteria (see
Highlight Sites), and analysis can be performed only on the chosen subset of sites.
Nucleotide Composition
Statistics | Nucleotide Composition
This command is visible only if the data consist of nucleotide sequences. MEGA
computes the base frequencies for each sequence as well as an overall average.
These will be displayed by domain in a Text Editor domain (if the domains have
been defined in Setup/Select Genes & Domains).
Nucleotide Pair Frequencies
Statistics | Nucleotide Pair Frequencies
This command is visible only if the data consists of nucleotide sequences. There
are two options available: one in which the nucleotide acid pairs are counted
bidirectionally site-by-site for the two sequences (giving rise to 16 different
nucleotide pairs), the other, in which the pairs are counted unidirectionally (10
nucleotide pairs). MEGA will compute the frequencies of these quantities for each
sequence as well as an overall average. They will be displayed in a Text Editor
domain by domain (if domains have been defined in Setup/Select Genes &
Domains).
Codon Usage
Statistics | Codon Usage
This command is visible only if the data contains protein-coding nucleotide
sequences. MEGA 4 computes the percent codon usage and the RCSU values for
each codon for all sequences included in the dataset. Results will be displayed in a
Text Editor domain (if domains have been defined in Setup/Select Genes &
Domains).
Amino Acid Composition
Statistics | Amino acid Composition
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This command is visible only if the data consists of amino acid sequences or if the
translated protein coding nucleotide sequences are displayed. MEGA will compute
the amino acid frequencies for each sequence as well as an overall average, which
will be displayed in a Text Editor domain (if domains have been defined in
Setup/Select Genes & Domains).
Use All Selected Sites
Statistics | Use All Selected Sites
Analysis is conducted on all sites in the sequences, irrespective of whether any
sites have been labeled or highlighted.
Use only Highlighted Sites
Statistics | Use only Highlighted Sites
Sites can be selected according to various criteria (see Highlight Sites), and
analyses will be performed only on the chosen subset of sites. All statistical
attributes will be based on these sites.
4.43 Distance Data Explorer
Distance Data Explorer
The Distance Data Explorer shows the pair-wise distance data. This explorer is
flexible and it provides useful functionalities for computing within group, among
group, and overall averages, as well as facilities for selecting data subsets.
This explorer consists of a number of regions as follows:
Menu Bar
File menu
Display menu
Average menu.
Help: This item brings up the help file.
Tool Bar
The tool bar provides quick access to a number of menu items.
General Utilities
•
•
: This icon brings up the Options dialog box to export the distance
matrix as a text file with options to control how MEGA writes the output
data.
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•
: This button brings up the dialog box for setting up, editing, and
selecting taxa and groups of taxa.
Distance Display Precision
•
•
: With each click of this button, the precision of the distance display
is decreased by one decimal place.
•
: With each click of this button, the precision of the distance display
is decreased by one decimal place.
•
Column Sizer: This is a slider that can be used to increase or decrease the
width of the columns that show the pairwise distances.
The 2-Dimensional Data Grid
This grid displays the pair-wise distances between all the sequences in the data in
the form of a lower or upper triangular matrix. The names of the sequences and
groups are the row-headers; the column headers are numbered from 1 to m, m
being the number of sequences. There is a column sizer button for the rowheaders, so you can increase or decrease the column size to accommodate the full
name of the sequences and groups.
•
Fixed Row: This is the first row in the data grid that displays the column
number.
•
Fixed Column: This is the first and the leftmost column in the data grid and
contains taxa names. Even if you scroll past the initial screen this column will
always be visible. To include a taxon in the data set for analysis, check the
associated box. In this column, you also can drag-and-drop taxa names to sort
them in the desired manner.
•
Rest of the Grid: The cells to the right of the first column and below the first row
contain the nucleotides or amino acids of the input data. Note that all cells
containing data corresponding to unselected sequences or genes/domains are
drawn in a light color.
Status bar
The status bar shows the sequence pair corresponding to the position of the cursor
when the cursor is on any distance value in the display.
File Menu (in Distance Data Explorer)
The File menu consists of three commands:
•
Select & Edit Taxa/Groups: This brings up a dialog box to categorize the taxa
into groups.
•
Export/Print Distances: This brings up a dialog box for writing pairwise
distances as a text file, with a choice of several formats.
•
Quit Viewer: This closes the Distance Data Explorer.
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Molecular Evolutionary Genetics Analysis
Display Menu (in Distance Data Explorer)
The Display menu consists of four main commands:
•
Show Only Selected Taxa: This is a toggle, showing a matrix of all or only
selected taxa.
•
Sort Taxa: This provides a submenu for sorting the order of taxa in one of three
ways: by input order, by taxon name or by group name.
•
Show Group Names: This is a toggle for displaying or hiding the group name
next to the name of each taxon, when available.
•
Change Font: This brings up the dialog box, which allows you to choose the
type and size of the font used to display the distance values.
Average Menu (in Distance Data Explorer)
This menu is used for the computation of average values using the selected taxa.
The following averaging options are available:
Overall: This computes and displays the overall average.
Within groups: This is enabled only if at least one group is defined. For each
group, an arithmetic average is computed for all valid pairwise comparisons and
results are displayed in the Distance Matrix Explorer. All incalculable withingroup averages are shown with a red "n/c".
Between groups: This is enabled only if at least two groups of taxa are defined.
For each between group average, an arithmetic average is computed for all valid
inter-group pairwise comparisons and results are displayed in the Distance Matrix
Explorer. All incalculable within group averages are shown with a red "n/c".
Net Between Groups: This computes net average distances between groups of taxa
and is enabled only if at least two groups of taxa with at least two taxa each are
defined. The net average distance between two groups is given by
dA = dXY – (dX - dY)/2
where, dXY is the average distance between groups X and Y, and dX and dY are
the mean within-group distances. All incalculable within group averages are
shown with a red "n/c".
Options dialog box
At the top of the options dialog box is an option for the output format (Publication
and MEGA) with the type of information that is output (distances) mentioned
beneath. Below this is the option for outputting the distance data as a lower left
triangular matrix or an upper right triangular matrix. On the right are options for
specifying the number of decimal places for the pairwise distances in the output,
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and the maximum number of distances per line in the matrix.
In addition there are three buttons, one to print or save the output, one to quit the
Options dialog box without exporting the data (Cancel), and the third to bring up
the help file (this file). The Print/Save button brings up the Distances Display Box,
where the distances are displayed as specified, with various options to edit, print
and save the output.
4.44 Text Editor
Using Text Editor
File Menu
New (in Text Editor)
File | New
Use this command to create a new file in the Text Editor.
Open (in Text Editor)
File | Open
Use this command to open an existing file in the Text Editor.
Reopen (in Text Editor)
File | Reopen
Choose this command to reopen a recently closed text file from the most-recentlyused-files list. When you close a text file in the Text Editor, it is added to the
Reopen list.
Select All (in Text Editor)
Edit | Select All
This is used to select (highlight) everything in the displayed file.
Go to Line (in Text Editor)
Edit | Go to Line #
This opens a small dialog box that allows you to enter a number indicating the line
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to which you want to move.
Show Line Numbers (in Text Editor)
Display | Show Line Numbers
This item can be checked (on) or un-checked (off) to show whether line numbers
are displayed next to the lines.
Word Wrap (in Text Editor)
Display | Word Wrap
This item can be checked (on) or un-checked (off) to show whether lines in the edit
window are automatically wrapped around based on the current window’s width.
Save (in Text Editor)
File | Save
This allows you to save the file currently being edited.
Save As (in Text Editor)
File | Save As
This command brings up the Save As dialog box, which allows you to choose the
directory, the filename and extension, and the type of file you wish to save. To
make a file suitable for loading as data in MEGA, you should save the file in
MEGA format (it is a plain ASCII text file). If there is already another file with the
same name, it will be overwritten
Print (in Text Editor)
File | Print
This command will print the currently displayed file to the selected printer.
Close File (in Text Editor)
File | Close File
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This closes the current file.
Exit Editor (in Text Editor)
File | Exit Editor
This closes the currently open file. If the file was modified, but the modifications
have not been saved, MEGA will ask whether to discard the changes. Note that
this command exits the Text Editor only, not MEGA.
Delete (in Text Editor)
Edit | Delete
This deletes the selected (highlighted) text. It is NOT copied to the clipboard.
Edit Menu
Cut (in Text Editor)
Edit | Cut
This command places a copy of the selected text on the Windows clipboard,
removing the original string. To paste the contents on the clipboard, use the Paste
command.
Copy (in Text Editor)
Edit | Copy
This places a copy of the selected text on the Windows clipboard, leaving the
original string untouched. To paste the contents on the clipboard, use the Paste
command.
Paste (in Text Editor)
Edit | Paste
This inserts the most recently copied text present on the Windows clipboard.
Undo (in Text Editor)
Edit | Undo
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Choose this command to undo your most recent action. Repeated use of this
command will undo each action, starting with the most recent and going to the
oldest. it has unlimited depth.
Font (in Text Editor)
Display | Set Font
Choose this command to activate a dialog box with which you can change the
display font used by the Text Editor. Since an ASCII text file does not have a font
attribute, it simply contains the text in the file. Therefore the change in the font
only affects the display. The new font is remembered by MEGA as your preferred
display font for the Text Editor.
Search Menu
Find (in Text Editor)
Search | Find
Choose this command to display the Find Text dialog box.
Find Again (in Text Editor)
Search | Find Again
Choose this to repeat the last Find command.
Replace (in Text Editor)
Search | Replace
This brings up a Search and Replace dialog box, which allows you to replace a text
string in the file currently being edited.
4.5 Visual Tools for Data Management
4.51 Setup/Select Genes & Domains
Data | Setup/Select Genes & Domains
The Setup/Select Genes & Domains dialog box allows you to view, specify, and edit genes
and domains and to label sites.
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4.52 Groups of taxa
A group of taxa is a set of one or more taxa. Members of a group can be specified
in the input data file, and created and edited in the Setup Taxa and Groups dialog.
Groups of taxa often are constructed based on their evolutionary relatedness. For
example, sequences may be grouped based on the geographic origin of the source
individual, or sequences from a multigene family may be arranged into groups
consisting of orthologous sequences.
4.53 Data Subset Selection
Sequence Data Subset Selection
Any subset of sequence data can be selected for analysis using the options in the
Data menu. You may:
1. Select Taxa (sequences) or Groups of taxa through the Setup/Select Taxa &
Groups dialog box,
2. Choose Domains and Genes through the Setup/Select Genes & Domains dialog
box,
Items 1 and 2 lead to the construction of a primary data subset, which is
maintained until it is modified in the two dialog boxes mentioned in the above
items or in the Sequence Data Explorer.
3. Select any combination of Codon Positions to use through the Analysis
Preferences/Options dialog box from the Data | Select Preferences menu item in
the main interface.
4. Choose to include only the Labeled Sites through the Data | Select Preferences
menu item.
5. Decide to enforce Complete-Deletion or Pairwise-Deletion of the missing data
and alignment gaps.
Items 3, 4, and 5 provide the second level of data subset options. You are given
relevant choices immediately prior to the start of the analysis. Therefore, these
choices are secondary in nature and are specific to the currently requested analysis.
The Analysis Preferences dialog box remembers them for your convenience and
provides them as a default the next time you conduct an analysis that utilizes those
options.
Distance Data Subset Selection
You may select Select Taxa (sequences) or Groups of taxa through the Setup/Select Taxa
& Groups dialog box to construct a distance matrix. You also can select sequences in the
Distance Data Explorer by clicking on the check marks next to the taxa names.
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5 Part IV: Evolutionary Analysis
5.1 Computing Basic Statistical Quantities for
Sequence Data
5.11 Basic Sequence Statistics
In the study of molecular evolution, it often is necessary to know some basic statistical
quantities, such as nucleotide frequencies, codon frequencies, and transition/transversion
ratios. The statistical quantities that can be computed by MEGA are discussed in this
section.
5.12 Nucleotide and Amino Acid Compositions
The relative frequencies of the four nucleotides (nucleotide composition) or of the 20
amino acid residues (amino acid composition) can be computed for one specific sequence
or for all sequences. For the coding regions of DNA, additional columns are presented for
the nucleotide compositions at the first, second, and third codon positions. All results are
presented domain-by-domain, if the dataset contains multiple domains. Results for the
amino acid composition are presented in a similar tabular form.
5.2 Computing Evolutionary Distances
5.21 Distance Models
Models for estimating distances
The evolutionary distance between a pair of sequences usually is measured by the number
of nucleotide (or amino acid) substitutions occurring between them. Evolutionary
distances are fundamental for the study of molecular evolution and are useful for
phylogenetic reconstructions and the estimation of divergence times. Most of the widely
used methods for distance estimation for nucleotide and amino acid sequences are included
in MEGA. In the following three sections, we present a brief discussion of these methods:
nucleotide substitutions, synonymous-nonsynonymous substitutions, and amino acid
substitutions. Further details of these methods and general guidelines for the use of these
methods are given in Nei and Kumar (2000). Note that in addition to the distance
estimates, MEGA 4 also computes the standard errors of the estimates using the analytical
formulas and the bootstrap method.
Distance methods included in MEGA in divided in three categories (Nucleotide,
Syn-nonsynonymous, and Amino acid):
Nucleotide
Sequences are compared nucleotide-by-nucleotide. These distances can be computed
for protein coding and non-coding nucleotide sequences.
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Part IV: Evolutionary Analysis
No. of differences
p-distance
Jukes-Cantor Model
with Rate Uniformity Among Sites
with Rate Variation Among Sites
Tajima-Nei Model
with Rate Uniformity and Pattern Homogeneity
with Rate Variation Among Sites
with Pattern Heterogeneity Between Lineages
with Rate Variation and Pattern Heterogeneity Heterogeneity
Kimura 2-Parameter Model
with Same Rate Among Sites
with Rate Variation Among Sites)
Tamura 3-Parameter Model
with Rate Uniformily and Pattern Homogeneity
with Rate Variation Among Sites
with Pattern Heterogeneity Between Lineages
with Rate Variation and Pattern Heterogeneity
Tamura-Nei Model
With Rate Uniformity and Pattern Homogeneity
with Rate Variation Among Sites
with Pattern Heterogeneity Between Lineages
with Rate Variation and Pattern Heterogeneity
Log-Det Method
with Pattern Heterogeneity Between Lineages
Maximum Composite Likelihood Model
with Rate Uniformity and Pattern Homogeneity
with Rate Variation Among Sites
with Pattern Heterogeneity Between Lineages
with Rate Variation and Pattern Heterogeneity
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Syn-Nonsynonymous
Sequences are compared codon-by-codon. These distances can only be computed for
protein-coding sequences or domains.
Nei-Gojobori Method
Modified Nei-Gojobori Method
Li-Wu-Luo Method
Pamilo-Bianchi-Li Method
Kumar Method
Amino Acid
Amino acid sequences are compared residue-by-residue. These distances can be computed
for protein sequences and protein-coding nucleotide sequences. In the latter case, proteincoding nucleotide sequences are automatically translated using the selected genetic code
table.
No. of differences
p-distance
Poisson Model
with Rate Uniformily Among Sites
with Rate Variation Among Sites
Equal Input Model
with Rate Uniformity and Pattern Homogeneity
with Rate Variation Among Sites
with Pattern Heterogeneity Between Lineages
with Rate Variation and Pattern Heterogeneity
Dayhoff and JTT Models
with Rate Uniformity Among Sites
with Rate Variation Among Sites
Nucleotide Substitution Models
No. of differences (Nucleotide)
This distance is the number of sites at which the two compared sequences differ. If you
are using the pairwise deletion option for handling gaps and missing data, it is important to
realize that this count does not normalize the number of differences based on the number
of valid sites compared, if the sequences contain alignment gaps. Therefore, we
recommend that if you use this distance you use the complete-deletion option.
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For this distance, MEGA provides facilities for computing the following quantities:
d: Transitions + Transversions : Number of different nucleotide sites.
s: Transitions only : Number of nucleotide sites with transitional differences.
v: Transversions only : Number of nucleotide sites with transversional differences.
R = s/v : Transition/transversions ratio.
L: No of valid common sites: Number of compared sites.
Formulas for computing these quantities and their variances are as follows.
Var(d) =
Var(s) =
Var(v) =
R=
Var(R) =
where
and
P and Q are the proportion of sites showing transitional and transversional differences,
respectively.
See also Nei and Kumar (2000), page 33.
p-distance (Nucleotide)
This distance is the proportion (p) of nucleotide sites at which two sequences being
compared are different. It is obtained by dividing the number of nucleotide differences by
the total number of nucleotides compared. It does not make any correction for multiple
substitutions at the same site, substitution rate biases (for example, differences in the
transitional and transversional rates), or differences in evolutionary rates among sites.
MEGA provides facilities for computing following p-distances and related quantities:
d: Transitions + Transversions : Proportion of nucleotide sites that are different.
s: Transitions only : Proportion of nucleotide sites with transitional differences.
v: Transversions only : Proportion of nucleotide sites with transversional differences.
R = s/v : Transition/transversions ratio.
L: No of valid common sites: Number of sites compared.
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Molecular Evolutionary Genetics Analysis
Formulas for computing these quantities are as follows:
Quantity
,
Formula
Variance
,
s,
,
v,
,
R,
,
where
and
P and Q are the proportion of sites showing transitional and transversional differences,
respectively.
See also Nei and Kumar (2000), page 33.
Jukes-Cantor distance
In the Jukes and Cantor (1969) model, the rate of nucleotide substitution is the same for all
pairs of the four nucleotides A, T, C, and G. As is shown below, the multiple hit
correction equation for this model produces a maximum likelihood estimate of the number
of nucleotide substitutions between two sequences. It assumes an equality of substitution
rates among sites (see the related gamma distance), equal nucleotide frequencies, and it
does not correct for higher rate of transitional substitutions as compared to transversional
substitutions.
The Jukes-Cantor model
MEGA provides facilities for computing the following quantities:
d: Transitions + Transversions : Number of nucleotide substitutions per site.
L: No of valid common sites: Number of sites compared.
Formulas for computing these quantities are as follows:
Distance
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Part IV: Evolutionary Analysis
where p is the proportion of sites with different nucleotides.
Variance
See also Nei and Kumar (2000), page 36.
Tajima-Nei distance
In real data, nucleotide frequencies often deviate substantially from 0.25. In this case the
Tajima-Nei distance (Tajima and Nei 1984) gives a better estimate of the number of
nucleotide substitutions than the Jukes-Cantor distance. Note that this assumes an equality
of substitution rates among sites and between transitional and transversional substitutions.
The Felsenstein-Tajima-Nei model
MEGA provides facilities for computing the following quantities for this method:
d: Transitions + Transversions : Number of nucleotide substitutions per site.
L: No of valid common sites: Number of sites compared.
Formulas for computing these quantities are as follows:
Distance
where p is the proportion of sites with different nucleotides and
where xij is the relative frequency of the nucleotide pair i and j, gi’s are the nucleotide
frequencies.
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Molecular Evolutionary Genetics Analysis
Variance
See also Nei and Kumar (2000), page 38.
Kimura 2-parameter distance
Kimura’s two parameter model (1980) corrects for multiple hits, taking into account
transitional and transversional substitution rates, while assuming that the four nucleotide
frequencies are the same and that rates of substitution do not vary among sites (see related
Gamma distance).
The Kimura 2-parameter model
MEGA 4 provides facilities for computing the following quantities:
Quantity
Description
d: Transitions +
Transversions
Number of nucleotide
substitutions per site.
s: Transitions only
Number of transitional
substitutions per site.
v: Transversions only
Number of transversional
substitutions per site.
R = s/v
Transition/transversions ratio.
L: No of valid
common sites
Number of sites compared.
Formulas for computing these quantities are as follows:
Distances
where P and Q are the frequencies of sites with transitional and transversional differences
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respectively, and
Variances
where
•
See also Nei and Kumar (2000), page 37.
Tamura 3-parameter distance
Tamura’s 3-parameter model corrects for multiple hits, taking into account differences in
transitional and transversional rates and G+C-content bias (1992). It assumes an equality
of substitution rates among sites.
The Tamura 3-parameter model
MEGA 4 provides facilities for computing the following quantities:
Quantity
Description
d: Transitions &
Transversions
Number of nucleotide substitutions per
site.
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Molecular Evolutionary Genetics Analysis
s: Transitions only
Number of transitional substitutions per
site.
v: Transversions only
Number of transversional substitutions
per site.
R = s/v
Transition/transversions ratio.
L: No of valid common
sites
Number of sites compared.
The formulas for computing these quantities are as follows:
Distances
where P and Q are the proportion of sites with transitional and transversional differences
respectively, and
Variances
where
See also Nei and Kumar (2000), page 39.
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Part IV: Evolutionary Analysis
Tamura-Nei distance
The Tamura-Nei model (1993) corrects for multiple hits, taking into account the
differences in substitution rate between nucleotides and the inequality of nucleotide
frequencies. It distinguishes between transitional substitution rates between purines and
transversional substitution rates between pyrimidines. It also assumes equality of
substitution rates among sites (see related gamma model).
The Tamura-Nei model
MEGA 4 provides facilities for computing the following quantities for this method:
Quantity
Description
d: Transitions &
Transversions
Number of nucleotide substitutions
per site.
s: Transitions only
Number of transitional substitutions
per site.
v: Transversions only
Number of transversional
substitutions per site.
R = s/v
Transition/transversions ratio.
L: No of valid
common sites
Number of sites compared.
Formulas for computing these quantities are as follows:
Distances
where P1 and P2 are the proportions of transitional differences between nucleotides A and
G, and between T and C, respectively, Q is the proportion of transversional differences,
gA, gC, gG, gT, are the respective frequencies of A, C, G and T, gR = gA + gG, gY, = gT +
gC, and
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Molecular Evolutionary Genetics Analysis
Variances
where
See also Nei and Kumar (2000), page 40.
Maximum Composite Likelihood Method
A composite likelihood is defined as a sum of related log-likelihoods. Since all pairwise
distances in a distance matrix have correlations due to the phylogenetic relationships
among the sequences, the sum of their log-likelihoods is a composite likelihood. Tamura et
al. (2004) showed that pairwise distances and the related substitution parameters are
accurately estimated by maximizing the composite likelihood. They also found that, unlike
the cases of ordinary independent estimation of each pairwise disntace, a complicated
model had virtually no disadvantage in the composite likelihood method for phylogenetic
analyses. Therefore, only the Tamura-Nei (1993) model is available for this method in
MEGA4 (see related Tamura-Nei distance). It assumes equality of substitution pattern
among lineages and of substitution rates among sites (see related gamma model and
heterogeneous patterns).
Gamma Distances
Computing the Gamma Parameter (a)
In the computation of gamma distances, it is necessary to know the gamma parameter (a).
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Part IV: Evolutionary Analysis
This parameter may be estimated from the dataset under consideration or you may use the
value obtained from previous studies. For estimating a, a substantial number of sequences
is necessary; if the number of sequences used is small, the estimate has a downward bias
(Zhang and Gu 1998). The current release of MEGA 4 does not contain any programs for
estimating a; however we plan to make them available in the future. Therefore you need
to use another program for estimating the a value. Some of the frequently used programs
that include this facility are PAUP* (Swofford 1998) for DNA sequences, PAML and
PAMP programs for DNA and protein sequences (Yang 1999), and GAMMA programs
from Gu and Zhang (1997).
Equal Input Model (Gamma)
In real data, amino acid frequencies usually vary among the different kinds of amino acids
and substitution rates are not uniform among sites. In this case, the correction based on the
equal input model gives a better estimate of the number of amino acid substitutions than
the Poisson correction distance. The rate variation among sites is modeled using the
Gamma distribution; for computing this distance you will need to provide a gamma
parameter (a).
MEGA provides facilities for computing the following quantities:
Quantity
Description
d: distance
Number of amino acid substitutions
per site.
L: No of valid common
sites
Number of sites compared.
Formulas used are:
Distance
where p is the proportion of different amino acid sites, a is the gamma parameter, gi is the
frequency of amino acid i, and
Variance
Jukes-Cantor Gamma distance
In the Jukes and Cantor (1969) model, the rate of nucleotide substitution is the same for all
pairs of the four nucleotides A, T, C, and G. The multiple hit correction equation for this
model, which is given below, produces a maximum likelihood estimate of the number of
nucleotide substitutions between two sequences, while relaxing the assumption that all
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Molecular Evolutionary Genetics Analysis
sites are evolving at the same rate. However, it assumes equal nucleotide frequencies and
does not correct for higher rate of transitional substitutions as compared to transversional
substitutions. If the rate variation among sites is modeled using the Gamma distribution,
you will need to provide a gamma parameter (a) for computing this distance.
The Jukes-Cantor model
MEGA provides facilities for computing the following p-distances and related quantities:
d: Transitions + Transversions : Number of nucleotide substitutions per site.
L: No of valid common sites: Number of sites compared.
The formulas for computing these quantities are as follows:
Distance
where p is the proportion of sites with different nucleotides and a is the gamma parameter.
Variance
See also Nei and Kumar (2000), page 36 and estimating gamma parameter.
Kimura gamma distance
Kimura’s two-parameter gamma model corrects for multiple hits, taking into account
transitional and transversional substitution rates and differences in substitution rates
among sites. Evolutionary rates among sites are modeled using the Gamma distribution,
and you will need to provide a gamma parameter for computing this distance.
The Kimura 2-parameter model
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Part IV: Evolutionary Analysis
MEGA 4 provides facilities for computing the following quantities:
Quantity
Description
d: Transitions +
Transversions
Number of nucleotide
substitutions per site.
s: Transitions only
Number of transitional
substitutions per site.
v: Transversions only
Number of transversional
substitutions per site.
R = s/v
Transition/transversions ratio.
L: No of valid common
sites
Number of sites compared.
The formulas for computing these quantities are as follows:
Distances
where P and Q are the respective total frequencies of transition type pairs and transversion
type pairs, a is the gamma parameter, and
Variances
where
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Molecular Evolutionary Genetics Analysis
See also Nei and Kumar (2000), page 44 and estimating gamma parameter.
Tajima Nei distance (Gamma rates)
In real data, nucleotide frequencies often deviate substantially from 0.25. In this case the
Tajima-Nei distance (Tajima and Nei 1984) gives a better estimate of the number of
nucleotide substitutions than the Jukes-Cantor distance. Note that this assumes an equality
of substitution rates among sites and between transitional and transversional substitutions.
The rate variation among sites is modeled using the gamma distribution, and you will need
to provide a gamma parameter (a) for computing this distance.
The Felsenstein-Tajima-Nei model
MEGA provides facilities for computing the following quantities for this method:
d: Transitions + Transversions : Number of nucleotide substitutions per site.
L: No of valid common sites: Number of sites compared.
The formulas for computing these quantities are as follows:
Distance
where p is the proportion of sites with different nucleotides, a is the gamma parameter, and
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Part IV: Evolutionary Analysis
where xij is the relative frequency of the nucleotide pair i and j, gi’s are the
nucleotide frequencies.
Variance
Tamura-Nei gamma distance
The Tamura-Nei (1993) distance with the gamma model corrects for multiple hits, taking
into account the different rates of substitution between nucleotides and the inequality of
nucleotide frequencies. In this distance, evolutionary rates among sites are modeled using
the gamma distribution. You will need to provide a gamma parameter for computing this
distance.
The Tamura-Nei model
MEGA 4 provides facilities for computing the following quantities for this method:
Quantity
Description
d: Transitions &
Transversions
Number of nucleotide substitutions per
site.
s: Transitions only
Number of transitional substitutions
per site.
v: Transversions only
Number of transversional substitutions
per site.
R = s/v
Transition/transversions ratio.
L: No of valid common
sites
Number of sites compared.
The formulas for computing these quantities are as follows:
Distances
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Molecular Evolutionary Genetics Analysis
where P1 and P2 are the proportions of transitional differences between nucleotides A and
G, and between T and C, respectively, Q is the proportion of transversional differences,
gA, gC, gG, gT, are the respective frequencies of A, C, G and T, gR = gA + gG, gY, = gT +
gC, a is the gamma parameter and
Variances
where
See also Nei and Kumar (2000), page 45 and estimating gamma parameter.
Tamura 3-parameter (Gamma)
Tamura’s 3-parameter model corrects for multiple hits, taking into account the differences
in transitional and transversional rates and the G+C-content bias (1992). Evolutionary
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Part IV: Evolutionary Analysis
rates among sites are modeled using the gamma distribution, and you will need to provide
a gamma parameter for computing this distance.
The Tamura 3-parameter model
MEGA 4 provides facilities for computing the following quantities:
Quantity
Description
d: Transitions &
Transversions
Number of nucleotide substitutions per
site.
s: Transitions only
Number of transitional substitutions per
site.
v: Transversions only
Number of transversional substitutions
per site.
R = s/v
Transition/transversions ratio.
L: No of valid common
sites
Number of sites compared.
The formulas for computing these quantities are as follows:
Distances
where P and Q are the proportion of sites with transitional and transversional differences,
respectively, a is the gamma parameter, and
Variances
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Molecular Evolutionary Genetics Analysis
where
Maximum Composite Likelihood (Gamma Rates)
The Tamura-Nei (1993) distance with the gamma model estimated by the composite
likelihood method (Tamura et al. 2004) corrects for multiple hits, taking into account the
different rates of substitution between nucleotides and the inequality of nucleotide
frequencies. In this distance, evolutionary rates among sites are modeled using the gamma
distribution. You will need to provide a gamma parameter for computing this distance. See
related Tamura-Nei gamma distance.
Heterogeneous Patterns
Tajima Nei Distance (Heterogeneous patterns)
In real data, nucleotide frequencies often deviate substantially from 0.25. In this case the
Tajima-Nei distance (Tajima and Nei 1984) gives a better estimate of the number of
nucleotide substitutions than the Jukes-Cantor distance. Note that this assumes an equality
of substitution rates among sites and between transitional and transversional substitutions.
When the nucleotide frequencies are different between the sequences, the modified
formula (Tamura and Kumar 2002) relaxes the assumption of substitution pattern
homogeneity.
The Felsenstein-Tajima-Nei model
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Part IV: Evolutionary Analysis
MEGA provides facilities for computing the following quantities for this method:
d: Transitions + Transversions : Number of nucleotide substitutions per site.
L: No of valid common sites: Number of sites compared.
Formulas for computing these quantities are as follows:
Distance
where p is the proportion of sites with different nucleotides and
where xij is the relative frequency of the nucleotide pair i and j, gi’s are the nucleotide
frequencies.
Variance can be estimated by the bootstrap method.
Tamura 3 parameter (Heterogeneous patterns)
Tamura’s 3-parameter model corrects for multiple hits, taking into account the differences
in transitional and transversional rates and the G+C-content bias (1992). It assumes an
equality of substitution rates among sites. When the G+C-contents are different between
the sequences, the modified formula (Tamura and Kumar 2002) relaxes the assumption of
substitution pattern homogeneity.
The Tamura 3-parameter model
MEGA 4 provides facilities for computing the following quantities:
Quantity
Description
d: Transitions &
Transversions
Number of nucleotide substitutions per
site.
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Molecular Evolutionary Genetics Analysis
s: Transitions only
Number of transitional substitutions per
site.
v: Transversions only
Number of transversional substitutions
per site.
R = s/v
Transition/transversions ratio.
L: No of valid common
sites
Number of sites compared.
Formulas for computing these quantities are as follows:
Distances
where P and Q are the proportion of sites with transitional and transversional differences,
respectively, and
The variances can be estimated by the bootstrap method. .
Tamura-Nei distance (Heterogeneous Patterns)
The Tamura-Nei model (1993) corrects for multiple hits, taking into account the
substitution rate differences between nucleotides and the inequality of nucleotide
frequencies. It distinguishes between transitional substitution rates between purines and
transversional substitution rates between pyrimidines. It assumes an equality of
substitution rates among sites (see related gamma model). When nucleotide frequencies
are different between the sequences, the modified formula (Tamura and Kumar 2002)
relaxes the assumption of substitution pattern homogeneity.
The Tamura-Nei model
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Part IV: Evolutionary Analysis
MEGA 4 provides facilities for computing the following quantities for this method:
Quantity
Description
d: Transitions &
Transversions
Number of nucleotide substitutions
per site.
s: Transitions only
Number of transitional substitutions
per site.
v: Transversions only
Number of transversional
substitutions per site.
R = s/v
Transition/transversions ratio.
L: No of valid
common sites
Number of sites compared.
Formulas for computing these quantities are as follows:
Distances
where P1 and P2 are the proportions of transitional differences between nucleotides A and
G, and between T and C, respectively, Q is the proportion of transversional differences,
gXA, gXC, gXG, gXT, are the respective frequencies of A, C, G and T of sequence X, gXR
= gXA + gXG and gXY = gXT + gXC, gA, gC, gG, gT, gR, and gY are the average
frequencies of the pair of sequences, and
The variances can be estimated by the bootstrap method.
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Molecular Evolutionary Genetics Analysis
Maximum Composite Likelihood (Heterogeneous Patterns)
The Tamura-Nei distance (1993) estimated by the composite likelihood method (Tamura et
al. 2004) corrects for multiple hits, taking into account the substitution rate differences
between nucleotides and the inequality of nucleotide frequencies. When the nucleotide
frequencies between the sequences are different, the expected proportions of observed
differences (P1, P2, Q) in the computation of the composite likelihood can be obtained by
the modified formulas according to Tamura and Kumar (2002) to relax the assumption of the
substitution pattern homogeneity. See related Tamura-Nei distance (Heterogeneous
Patterns).
Gamma Rates
Equal Input Model (Gamma rates and Heterogeneous Patterns)
In real data, amino acid frequencies usually vary among different kind of amino acids.
Therefore, the correction based on the equal input model gives a better estimate of the
number of amino acid substitutions than the Poisson correction distance. If you are
computing the rate variation among sites using the Gamma distribution, you will need to
provide a gamma parameter (a). When the amino acid frequencies are different between
the sequences, the modified formula (Tamura and Kumar 2002) relaxes the estimation
bias.
MEGA provides facilities for computing the following quantities:
Quantity
Description
d: distance
Number of amino acid substitutions
per site.
L: No of valid common
sites
Number of sites compared.
Formulas used are:
Distance
where p is the proportion of different amino acid sites, a is the gamma parameter, gXi is
the frequency of amino acid i for sequence X, gi is the average frequency for the pair of the
sequences, and
The variance of d can be estimated by the bootstrap method.
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Part IV: Evolutionary Analysis
Tajima Nei Distance (Gamma Rates and Heterogeneous patterns)
In real data, nucleotide frequencies often deviate substantially from 0.25. In this case the
Tajima-Nei distance (Tajima and Nei 1984) gives a better estimate of the number of
nucleotide substitutions than the Jukes-Cantor distance. Note that this assumes an equality
of substitution rates among sites and between transitional and transversional substitutions.
The rate variation among sites is modeled using the gamma distribution, and you will need
to provide a gamma parameter (a) for computing this distance. When the nucleotide
frequencies are different between the sequences, the modified formula (Tamura and
Kumar 2002) relaxes the assumption of substitution pattern homogeneity.
The Felsenstein-Tajima-Nei model
MEGA provides facilities for computing the following quantities for this method:
d: Transitions + Transversions : Number of nucleotide substitutions per site.
L: No of valid common sites: Number of sites compared.
The formulas for computing these quantities are as follows:
Distance
where p is the proportion of sites with different nucleotides, a is the gamma parameter, and
where xij is the relative frequency of the nucleotide pair i and j, gi’s are the
nucleotide frequencies.
Variance can be estimated by the bootstrap method.
Tamura-Nei distance (Gamma rates and Heterogeneous patterns)
The Tamura-Nei (1993) distance with the gamma model corrects for multiple hits, taking
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Molecular Evolutionary Genetics Analysis
into account the rate substitution differences between nucleotides and the inequality of
nucleotide frequencies. In this distance, evolutionary rates among sites are modeled using
the gamma distribution. You will need to provide a gamma parameter for computing this
distance. When the nucleotide frequencies between the sequences are different, the
modified formula (Tamura and Kumar 2002) relaxes the assumption of the substitution
pattern homogeneity.
The Tamura-Nei model
MEGA 4 provides facilities for computing the following quantities for this method:
Quantity
Description
d: Transitions &
Transversions
Number of nucleotide substitutions per
site.
s: Transitions only
Number of transitional substitutions
per site.
v: Transversions only
Number of transversional substitutions
per site.
R = s/v
Transition/transversions ratio.
L: No of valid common
sites
Number of sites compared.
The formulas for computing these quantities are as follows:
Distances
where P1 and P2 are the proportions of transitional differences between nucleotides A and
G, and between T and C, respectively, Q is the proportion of transversional differences,
gXA, gXC, gXG, gXT, are the respective frequencies of A, C, G and T of sequence X, gXR
= gXA + gXG and gXY = gXT + gXC, gA, gC, gG, gT, gR, and gY are the average
frequencies of the pair of sequences, a is the gamma parameter and
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Part IV: Evolutionary Analysis
The variances can be estimated by the bootstrap method.
Tamura 3 parameter (Gamma rates and Heterogeneous patterns)
Tamura’s 3-parameter model corrects for multiple hits, taking into account the differences
in transitional and transversional rates and the G+C-content bias (1992). Evolutionary
rates among sites are modeled using the gamma distribution, and you will need to provide
a gamma parameter for computing this distance. When the G+C-contents between the
sequences are different, the modified formula (Tamura and Kumar 2002) relaxes the
assumption of substitution pattern homogeneity.
The Tamura 3-parameter model
MEGA 4 provides facilities for computing the following quantities:
Quantity
Description
d: Transitions &
Transversions
Number of nucleotide substitutions per
site.
s: Transitions only
Number of transitional substitutions per
site.
v: Transversions only
Number of transversional substitutions
per site.
R = s/v
Transition/transversion ratio.
L: No of valid common
sites
Number of sites compared.
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Molecular Evolutionary Genetics Analysis
Formulas for computing these quantities are as follows:
Distances
where P and Q are the proportion of sites with transitional and transversional differences,
respectively, a is the gamma parameter, and
The variances can be estimated by the bootstrap method.
Maximum Composite Likelihood (Gamma Rates and Heterogeneous Patterns)
The Tamura-Nei (1993) distance estimated by the composite likelihood method (Tamura et
al. 2004) with the gamma model corrects for multiple hits, taking into account the rate
substitution differences between nucleotides and the inequality of nucleotide frequencies. In
this distance, evolutionary rates among sites are modeled using the gamma distribution.
You will need to provide a gamma parameter for computing this distance. When the
nucleotide frequencies between the sequences are different, the expected proportions of
observed differences (P1, P2, Q) in the computation of the composite likelihood can be
obtained by the modified formulas according to Tamura and Kumar (2002) to relax the
assumption of the substitution pattern homogeneity.
Amino Acid Substitution Models
No. of differences (Amino acids)
This distance is the number of sites at which two sequences being compared are different.
If the sequences contain alignment gaps or missing data and you are using the pairwise
deletion option, you must realize that this count does not normalize the number of
differences based on the number of valid sites compared. Therefore, if you use this
distance, we recommend that you use the complete-deletion option.
MEGA computes the following quantities:
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Part IV: Evolutionary Analysis
Quantity
Description
d: distance
Number of sites
different.
L: No of valid common
sites
Number of sites
compared.
The formulas used are:
Quantity
Formula
Variance
None
See also Nei and Kumar (2000), page 18.
p-distance (Amino acids)
This distance is the proportion (p) of amino acid sites at which the two sequences to be
compared are different. It is obtained by dividing the number of amino acid differences by
the total number of sites compared. It does not make any correction for multiple
substitutions at the same site or differences in evolutionary rates among sites.
MEGA provides facilities to compute the following quantities:
Quantity
Description
d: distance
Proportion of amino acid sites
different.
L: No of valid common
sites
Number of sites compared.
The formulas used are:
Quantity
where
Formula
Variance
is the number of amino acids that are different between two aligned sequences.
See also Nei and Kumar (2000), page 18.
Equal Input Model (Amino acids)
In real data, frequencies usually vary among different kind of amino acids. In this case, the
correction based on the equal input model gives a better estimate of the number of amino
acid substitutions than the Poisson correction distance. Note that this assumes an equality
of substitution rates among sites and the homogeneity of substitution patterns between
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Molecular Evolutionary Genetics Analysis
lineages.
MEGA provides facilities to compute the following quantities:
Quantity
Description
d: distance
Number of amino acid substitutions
per site.
L: No of valid common
sites
Number of sites compared.
The formulas used are:
Distance
where p is the proportion of different amino acid sites, gi is the frequency of amino acid i,
and
Variance
Poisson Correction (PC) distance
The Poisson correction distance assumes equality of substitution rates among sites and
equal amino acid frequencies while correcting for multiple substitutions at the same site.
MEGA provides facilities to compute the following quantities:
Quantity
Description
d: distance
Number of amino acid substitutions
per site.
L: No of valid common
sites
Number of sites compared.
Formulas used are:
Quantity
Formula
Variance
See also Nei and Kumar (2000), page 20.
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Part IV: Evolutionary Analysis
Dayhoff and JTT Models
The PAM and JTT distances correct for multiple substitutions based on the model
of amino acid substitution described as substitution-rate matrices. The PAM
distance uses the PAM 001 matrix (p. 348 in Dayhoff 1979) and the JTT distance
uses the JTT matrix (Jones et al. 1992). Using a substitution-rate matrix (Q), the
matrix (F), which consists of the observed proportions of amino acid pairs between
a pair of sequences with their divergence time t, is given by the following equation
where A denotes the diagonal matrix of the equilibrium amino acid frequencies for
Q. From this equation, the evolutionary distance d = 2tQ can be iteratively
computed by a maximum-likelihood method. The eigen values for the PAM and
JTT matrices required in this computation were obtained from the program source
code of PHYLIP version 3.6 (Felsenstein et al. 1993-2001).
MEGA provides facilities for computing the following quantities:
Quantity
Description
d: distance
Number of amino acid substitutions
per site.
L: No of valid common
sites
Number of sites compared.
The variance of d can be estimated by the bootstrap method.
Gamma Distances
Dayhoff and JTT distances (Gamma rates)
The PAM and JTT distances correct for multiple substitutions based on a model of
amino acid substitution described as substitution-rate matrices. The PAM distance
uses PAM 001 matrix (p. 348 in Dayhoff 1979) and the JTT distance uses JTT
matrix (Jones et al. 1992). The matrix (F) uses a substitution-rate matrix (Q) and
the gamma distribution with parameter a for the rate variation among sites. It
consists of the observed proportions of amino acid pairs with their divergence time
t, given by the following equation
where A denotes the diagonal matrix of the equilibrium amino acid frequencies for
Q. From this equation, the evolutionary distance d = 2tQ can be computed
iteratively by a maximum-likelihood method. The eigen values for the PAM and
JTT matrices required in this computation were obtained from the program source
code of PHYLIP version 3.6 (Felsenstein et al. 1993-2001).
MEGA provides facilities for computing the following quantities:
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Molecular Evolutionary Genetics Analysis
Quantity
Description
d: distance
Number of amino acid substitutions
per site.
L: No of valid common
sites
Number of sites compared.
The variance of d can be estimated by the bootstrap method.
Gamma distance (Amino acids)
The Gamma distance improves upon the Poisson correction distance by taking care of the
inequality of the substitution rates among sites. For this purpose, you will need to provide
the gamma shape parameter (a).
For estimating the Dayhoff distance, use a = 2.25 (see Nei and Kumar [2000], page 21 for
details).
For computing Grishin’s distance, use a = 0.65. 23 (see Nei and Kumar [2000], page 23
for details)
MEGA provides facilities to compute the following quantities:
Quantity
Description
d: distance
Number of amino acid substitutions
per site.
L: No of valid common
sites
Number of sites compared.
Formulas used are:
Quantity
Formula
Variance
See also Nei and Kumar (2000), page 23 and estimating gamma parameter.
Heterogeneous Patterns
Equal Input Model (Heterogeneous Patterns)
In real data, amino acid frequencies usually vary among different kinds of amino acids. In
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Part IV: Evolutionary Analysis
this case, a correction based on the equal input model gives a better estimate of the number
of amino acid substitutions than does the Poisson correction distance. Note that this
assumes an equality of substitution rates among sites. When the amino acid frequencies are
different between the sequences, the modified formula (Tamura and Kumar 2002) relaxes
the estimation bias.
MEGA provides facilities for computing the following quantities:
Quantity
Description
d: distance
Number of amino acid substitutions
per site.
L: No of valid common
sites
Number of sites compared.
Formulas used are:
Distance
where p is the proportion of different amino acid sites, gXi is the frequency of amino acid i
for sequence X, gi is the average frequency for the pair of the sequences, and
The variance of d can be estimated by the bootstrap method.
Synonymouse and Nonsynonymous Substitution Models
Nei-Gojobori Method
This method computes the numbers of synonymous and nonsynonymous substitutions and
the numbers of potentially synonymous and potentially nonsynonymous sites (Nei and
Gojobori 1986). Based on these estimates, MEGA can be asked to produce the following
quantities:
Number of differences (Sd or Nd)
These are simple counts of the number of synonymous (Sd) and nonsynonymous
(Nd) differences. To compare these two numbers, you must use the p-distance
because the number of potential synonymous sites is much smaller than the number
of nonsynonymous sites.
p-distance (pS or pN)
The count of the number of synonymous differences (Sd) is normalized using the
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Molecular Evolutionary Genetics Analysis
possible number of synonymous sites (S). A similar computation can be made for
nonsynonymous differences.
Jukes-Cantor correction (dS or dN)
The p-distances computed above can be corrected to account for multiple
substitutions at the same site.
Difference between synonymous and nonsynonymous distances
MEGA 4 can compute differences between the synonymous and nonsynonymous
distances. These statistics are useful in conducting tests for selection.
Number of Sites (S or N)
The numbers of potential synonymous and nonsynonymous sites can be computed
using this option. For each pair of sequences, the average number of synonymous or
nonsynonymous sites is reported.
The formulas for computing these quantities are:
Quanti
ty
Formula
Variance
Dp
Dd
See also Nei and Kumar (2000), page 52
Modified Nei-Gojobori Method
The modified Nei-Gojobori distance differs from the original Nei-Gojobori formulation in
one way: transitional and transversional substitutions are no longer assumed to occur with
the same frequency. Thus the user is requested to provide the Transition/Transversion (R)
ratio. When R = 0.5, this method becomes identical to the Nei-Gojobori method. When R
> 0.5, the number of synonymous sites is less than estimated using Nei-Gojobori method
and consequently, the number of nonsynonymous sites will be larger than estimated with
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Part IV: Evolutionary Analysis
the original Nei-Gojobori (Nei and Gojobori 1986) approach.
Number of differences (Sd or Nd)
These are counts of the numbers of synonymous (Sd) and nonsynonymous (Nd)
differences. To compare these two numbers you must use the p-distance because
the number of potential synonymous sites is much smaller than the number of
nonsynonymous sites.
p-distance (pS or pN)
The count of the number of synonymous differences (Sd) is normalized using the
number of potential synonymous sites (S). A similar computation can be made for
nonsynonymous differences.
Jukes-Cantor correction (dS or dN)
The p-distances computed above can be corrected to account for multiple
substitutions at the same site.
Difference between synonymous and nonsynonymous distances
MEGA 4 can compute differences between synonymous and nonsynonymous
distances. These statistics are useful when conducting tests for selection.
Number of Sites (S or N)
Numbers of potentially synonymous and nonsynonymous sites can be computed
using this option. For each pair of sequences, the average number of synonymous or
nonsynonymous sites is reported.
The formulas for computing these quantities are:
Quant
ity
Formula
Variance
D
140
Molecular Evolutionary Genetics Analysis
See also Nei and Kumar (2000), page 52.
Li-Wu-Luo Method
In this method (Li et al 1985), each site in a codon is allocated to 0-fold, 2-fold or 4-fold
degenerate categories. For computing distances, all 0-fold and two-thirds of the 2-fold
sites are considered nonsynonymous, whereas one-third of the 2-fold and all of the 4-fold
sites are considered synonymous. The observed transitional and transversional differences
between codons then are partitioned into those occurring at 0-fold, 2-fold and 4-fold
degenerate sites. Based on this information, the following quantities can be estimated.
Synonymous distance
This is the number of synonymous substitutions per synonymous site.
Nonsynonymous distance
This is the number of nonsynonymous substitutions per nonsynonymous site.
Substitutions at the 4-fold degenerate sites
This is the number of substitutions per 4-fold degenerate site; it is useful for
measuring the rate of neutral evolution.
Substitutions at the 0-fold degenerate sites
This is the number of substitutions per 0-fold degenerate site; it is useful for
measuring the rate of amino acid sequence evolution.
Number of 4-fold degenerate sites
This is the estimate of the number of 4-fold degenerate sites, computed by averaging
the number of 4-fold degenerate sites in the two sequences, compared.
Number of 0-fold degenerate sites
This is the estimate of the number of 0-fold degenerate sites, computed by averaging
the number of 0-fold degenerate sites in the two sequences, compared.
Difference between synonymous and nonsynonymous distances
This computes the differences between the synonymous and nonsynonymous
distances. These statistics are useful for conducting tests of selection.
The formulas for computing these quantities are:
Quant
it
Formula
Variance
141
Part IV: Evolutionary Analysis
ity
D
Here,
are the number of 0-fold, 2-fold and 4-fold degenerate sites, respectively.
, and
, where
,
,
,
Pi and Qi are the proportions of i-fold degenerate sites that show transitional and
transversional differences, respectively.
,
See also Nei and Kumar (2000), page 62.
Pamilo-Bianchi-Li Method
This method (Pamilo and Bianchi 1993; Li 1993) is a modification of Li, Wu and Luo's
method. The only difference concerns the allocation of 2-fold sites to synonymous and
nonsynonymous categories. Rather than assuming an equal transition and transversion rate,
the rate is inferred from the observed number of transitions and transversions at the 4-fold
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Molecular Evolutionary Genetics Analysis
degenerate sites. Based on this information, the following quantities can be estimated:
Synonymous distance
This is the number of synonymous substitutions per synonymous site.
Nonsynonymous distance
This is the number of nonsynonymous substitutions per nonsynonymous site.
Substitutions at the 4-fold degenerate sites (d4)
This is the number of substitutions per 4-fold degenerate site; it is useful for
measuring the rate of neutral evolution.
Substitutions at the 0-fold degenerate sites (d0)
This is the number of substitutions per 0-fold degenerate site; it is useful for
measuring the rate of amino acid sequence evolution.
Number of 4-fold degenerate sites(L4)
The estimate of the number of 4-fold degenerate sites, computed by averaging the
number of 4-fold degenerate sites in the two sequences, compared.
Number of 0-fold degenerate sites (L0)
The estimate of the number of 0-fold degenerate sites, computed by averaging the
number of 0-fold degenerate sites in the two sequences, compared.
Difference between synonymous and nonsynonymous distances (D)
This computes the differences between the synonymous and nonsynonymous
distances. These statistics are useful for conducting tests of selection.
The formulas for computing these quantities are:
Quanti
ty
Formula
Variance
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Part IV: Evolutionary Analysis
d4
d0
D
Ai
Bi
Here,
are the number of 0-fold, 2-fold and 4-fold degenerate sites, respectively.
, and
, where
,
,
,
Pi and Qi are the proportions of i-fold degenerate sites that show transitional and
transversional differences, respectively.
,
See also Nei and Kumar (2000), page 64.
Kumar Method
This method is a modification of the Pamilo-Bianchi-Li and Comeron (1995) methods and
is able to handle some problematic degeneracy class assignments (see a detailed
description below). It computes the following quantities:
Synonymous distance
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Molecular Evolutionary Genetics Analysis
This is the number of synonymous substitutions per synonymous site.
Nonsynonymous distance
This is the number of nonsynonymous substitutions per nonsynonymous site.
Substitutions at the 4-fold degenerate sites
This is the number of substitutions per 4-fold degenerate site. It is useful for
measuring the rate of neutral evolution.
Substitutions at the 0-fold degenerate sites
This is the number of substitutions per 0-fold degenerate site. It is useful for
measuring the rate of amino acid sequence evolution.
Number of 4-fold degenerate sites
This is the estimate of the number of 4-fold degenerate sites, computed by averaging
the number of 4-fold degenerate sites in the two sequences, compared.
Number of 0-fold degenerate sites
This is the estimate of the number of 0-fold degenerate sites, computed by averaging
the number of 0-fold degenerate sites in the two sequences, compared.
Difference between synonymous and nonsynonymous distances
This computes the differences between the synonymous and nonsynonymous
distances. These statistics are useful for conducting tests of selection.
Kumar’s modification of the PBL method:
The treatment of arginine and isoleucine codons in the Li-Wu-Luo and the PamiloBianchi-Li methods is arbitrary, which sometimes creates a problem because the arginine
codons occur quite frequently. Comeron (1995) addressed this problem by dividing the 2fold degenerate sites into two groups: 2S-fold and 2V-fold. The 2S-fold refers to sites in
which the transitional change is synonymous and the two transversional changes are
nonsynonymous, whereas the 2V-fold represents sites in which the transitional change is
nonsynonymous and the transversional changes are synonymous. Although these
definitions help in correcting some of the inaccurate classifications of synonymous and
nonsynonymous sites (e.g., methionine codons), they do not solve the problem completely.
For example, consider mutations in the first nucleotide position of the arginine codon:
CGG produces TGG (Trp), AGG (Arg), or GGG (Gly). The transitional change (C to T)
results in a nonsynonymous change. Of the two transversional substitutions, one (C to A)
results in a synonymous change, while the other (C to G) results in a nonsynonymous
change. Therefore, this nucleotide site is neither a 2S-fold nor a 2V-fold site. Thus, the
first position of three arginine codons (CGU, CGC, and CGA) and the third position of
two isoleucine codons (ATT and ATC) cannot be assigned to any of the Comeron (1995)
categories. For this reason, Comeron (personal communication) used a more complicated
classification of codons when he wrote his computer program. For example, the first
position of arginine codon CGG was assigned to a 2V-fold site with a probability of one-
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third and to a 0-fold site with a probability of two-thirds. Similar assignments are used by
W.-H. Li (personal communication) in his computer program.
Since the nucleotide site assignments discussed above are quite arbitrary and may not
apply to all known genetic code tables, Kumar developed another method that uses the
PBL method for any genetic code table. In this version, nucleotide sites are first classified
into 0-fold, 2-fold, and 4-fold degenerate sites. The 2-fold degenerate sites are further
subdivided into simple 2-fold and complex 2-fold degenerate sites. Simple 2-fold sites are
those at which the transitional change results in a synonymous substitution and the two
transversional changes result in nonsynonymous substitutions. All other 2-fold sites,
including those for the three isoleucine codons, belong to the complex 2-fold site category.
If we use this definition, all nucleotide sites can be classified into the five groups shown in
the following table.
Table.
Degeneracy ->
No. of sites ->
0-
Simple 2-
fold
fold
L0
L2S
Complex 2-fold
4fold
L2C
L4
Syn
Nonsyn
Transition (s)
s0
s2
s2S
s2N
s4
Transversion (v)
v0
V2
v2S
v2N
v4
Here, L0, L2S, L2C, and L4 are the numbers of 0-fold, simple 2-fold, complex 2-fold, and
4-fold degenerate sites, respectively.
Once this table is filled using the observed counts for a given pair of sequences, we
compute the proportions of transitional (Pi) and transversional (Qi) differences for the ifold degenerate site in the following way:
From these quantities, we compute the Ai and Bi as in the PBL method. Then using L2 =
L2C + L2S, we apply the formulas for the PBL method.
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Molecular Evolutionary Genetics Analysis
See also Nei and Kumar (2000), page 64.
5.22 Specifying Distance Estimation Options
Analysis Preferences (Distance Computation)
In this dialog box you can select and view the desired options in the Options Summary.
Options are organized in logical sections. A lime square in the right-most cell in a row
indicates that you have a choice regarding the attribute in that row The three primary sets
of options available in this dialog box are:
Analysis
Compute
Use this to specify whether to compute Distances only or Distances and Standard
Errors. If you select the latter, then you are given a choice as to how to compute it
in the Standard Error Computation box.
Standard Error Computation By
This row is visible only if you have chosen Distances and Std. Err in the
Compute row. You may choose to use analytical formulas or the bootstrap
method to calculate standard errors depending on the type of distance
computed. Whenever the standard errors are estimated by the bootstrap
method, you will be prompted for the number of bootstrap replicates and a
random number seed.
When you compute average distance or diversity, only the bootstrap method is
available for computing standard errors.
Include Sites
These are options for handling gaps or missing data, including or excluding codon
positions, and restricting the analysis to labeled sites, if applicable.
Gaps and Missing Data
You may choose to remove all sites containing alignment gaps and missing
information before the calculation begins (Complete-deletion option). Alternatively,
you may choose to retain all such sites initially, excluding them as necessary in the
pairwise distance estimation (Pairwise-deletion option).
Codon Positions
Click on the ellipses or the lime square, for the option of selecting any combination
of 1st, 2nd, 3rd, and non-coding positions for analysis. This option is available only if
the nucleotide sequences contain protein-coding regions and you have selected a
nucleotide-by-nucleotide analysis.
Labeled Sites
This option is available only if some or all of the sites have associated labels. By
clicking on the ellipses, you will be provided with the option of including sites with
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Part IV: Evolutionary Analysis
selected labels. If you choose to include only labeled sites, then these sites will be
the first extracted from the data. Then all other options mentioned above will be
enforced. Note that labels associated with all three positions in the codon must be
included for a full codon to be incorporated in the analysis.
Substitution Model
In this set of options, you choose the various attributes of the substitution models.
Model
Here you select a stochastic model for estimating evolutionary distance by clicking
on the ellipses to the right of the currently selected model (click on the lime square
to select this row first). This will reveal a menu containing many different distance
methods and models.
Substitutions to Include
Depending on the distance model or method selected, the evolutionary distance can
be teased into two or more components. By clicking on the drop-down button (first
click on the lime square to select this row), you will be provided with a list of
components relevant to the chosen model.
Transition/Transversion Ratio
This option will be visible if the chosen model requires you to provide a value for
the Transition/Transversion ratio (R).
Pattern among Lineages
This option becomes available if the selected model has formulas that allow the
relaxation of the assumption of homogeneity of substitution patterns among
lineages.
Rates among Sites
This option becomes available if the selected distance model has formulas that allow
rate variation among sites. If you choose gamma-distributed rates, then the Gamma
parameter option becomes visible.
Distance Model Options
With this option, you can choose the general attributes of the substitution models for DNA
and protein sequence evolution.
Model
You can select a stochastic model for estimating evolutionary distances by clicking on the
ellipses to the right of the currently selected model (click on the lime square to select this
row first). This will reveal a menu containing many different distance methods/models.
Transition/Transversion Ratio
This option will be visible if the chosen model requires you to provide a value for the
Transition/Transversion ratio (R).
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Molecular Evolutionary Genetics Analysis
Pattern among Lineages
This option becomes available if the distance model you have selected has formulas that
allow the relaxation of the assumption of homogeneity of substitution patterns among
lineages.
Rates among Sites
This option becomes available if the distance model you have selected has
formulas that allow rate variation among sites. If you choose gamma distributed
rates, then the Gamma parameter option becomes visible.
Bootstrap method to compute standard error of distance estimates
When you choose the bootstrap method for estimating the standard error, you must specify
the number of replicates and the seed for the psuedorandom number generator. In each
bootstrap replicate, the desired quantity is estimated and the standard deviation of the
original values are computed (see Nei and Kumar [2000], page 25 for details).
It is possible that in some bootstrap replicates the quantity you desire is not calculable for
statistical or technical reasons. In these cases, MEGA will discard the results of the
bootstrap replicates and its final estimate will be the results of all valid replicates. This
means that the number of bootstrap replicates used can be smaller than the number
specified by the user. However, if the number of valid bootstrap replicates is < 25, then
MEGA will report that the standard error cannot be computed (an "n/c" swill appear in the
result window).
5.23 Compute Pariwise
5.24 Compute Means
5.25 Compute Sequence Diversity
5.3 Constructing Phylogenetic Trees
5.31 Phylogenetic Inference
Reconstruction of the evolutionary history of genes and species is currently one of
the most important subjects in molecular evolution. If reliable phylogenies are
produced, they will shed light on the sequence of evolutionary events that
generated the present day diversity of genes and species and help us to understand
the mechanisms of evolution as well as the history of organisms.
Phylogenetic relationships of genes or organisms usually are presented in a treelike
form with a root, which is called a rooted tree. It also is possible to draw a tree
without a root, which is called an unrooted tree. The branching pattern of a tree is
called a topology.
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There are numerous methods for constructing phylogenetic trees from molecular
data (Nei and Kumar 2000). They can be classified into Distance methods,
Parsimony methods, and Likelihood methods. These methods are explained in
Swofford et al. 1996, Li (1997), Page and Holmes (1998), and Nei and Kumar
(2000).
5.32 NJ/UPGMA Methods
Analysis Preferences (NJ/UPGMA)
In this dialog box, you can view and select desired options in the Options Summary.
Options are organized in logical sections. A lime square in the right cell of a row indicates
that you have a choice for that attribute. The three primary sets of options available in this
dialog box are:
Phylogeny Test and Options
To assess the reliability of a phylogenetic tree, MEGA provides two different
types of tests: the Bootstrap test and the Interior branch test. Both of these
tests use the bootstrap re-sampling strategy, so you need to enter the
number of replicates and a starting random seed. For a given data set
applicable tests and the phylogeny inference method are enabled.
Include Sites
These are options for handling gaps and missing data, including or excluding codon
positions, and restricting the analysis to labeled sites, if applicable.
Gaps and Missing Data
You may choose to remove all sites containing alignment gaps and missinginformation before the calculation begins using the Complete-deletion option.
Alternatively, you may choose to retain all such sites initially, excluding them as
necessary using the Pairwise-deletion option.
Codon Positions
By clicking on the ellipses or the lime square, you may select any combination of
1st, 2nd, 3rd, and non-coding positions for analysis. This option is available only if
the nucleotide sequences contain protein-coding regions and you have selected a
nucleotide-by-nucleotide analysis.
Labeled Sites
This option is available only if there are labels associated with some or all of
the sites in the data. By clicking on the ellipses, you will have the option of
including sites with selected labels. If you chose to include only labeled
sites, then these sites will be first extracted from the data and all other
options mentioned above will be enforced. Note that labels associated with
all three positions in the codon must be included for a full codon in the
analysis.
Substitution Model
In this set of options, you can choose various attributes of the substitution models
for DNA and protein sequences.
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Molecular Evolutionary Genetics Analysis
Model
By clicking on the ellipses to the right of the currently selected model, you may
select a stochastic model (method) for estimating evolutionary distance (click on the
lime square to select this row first). This will reveal a menu containing many
different distance methods and models.
Substitutions to Include
Depending on the distance model or method selected, the evolutionary distance can
be teased into two or more components. By clicking on the drop-down button (first
click on the lime square to select this row), you will be provided with a list of
components relevant to the chosen model.
Transition/Transversion Ratio
This option will be visible if the chosen model requires you to provide a value for
the Transition/Transversion ratio (R).
Pattern among Lineages
This option becomes available if the selected model has formulas that allow the
relaxation of the assumption of homogeneity of substitution patterns among
lineages.
Rates among Sites
This option becomes available if the selected distance model has formulas
that allow rate variation among sites. If you choose gamma-distributed
rates, then the Gamma parameter option becomes visible.
5.33 Minimum Evolution Method
Minimum Evolution
In the ME method, distance measures that correct for multiple hits at the same sites
are used, and a topology showing the smallest value of the sum of all branches (S)
is chosen as an estimate of the correct tree. However, the construction of a
minimum evolution tree is time-consuming because, in principle, the S values for
all topologies must be evaluated. The number of possible topologies (unrooted
trees) rapidly increases with the number of taxa so it becomes very difficult to
examine all topologies. In this case, one may use the neighbor-joining method.
While the NJ tree is usually the same as the ME tree, when the number of taxa is
small the difference between the NJ and ME trees can be substantial (reviewed in
Nei and Kumar 2000). In this case if a long DNA or amino acid sequence is used,
the ME tree is preferable. When the number of nucleotides or amino acids used is
relatively small, the NJ method generates the correct toplogy more often than does
the ME method (Nei et al. 1998, Takahashi and Nei 2000). In MEGA, we have
provided the close-neighbor-interchange search to examine the neighborhood of
the NJ tree to find the potential ME tree.
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Part IV: Evolutionary Analysis
Analysis Preferences (Minimum Evolution)
In this dialog box you can select and view desired options in the Options Summary.
Options are organized in logical sections. A lime square in the right cell of a row indicates
that you have a choice for that particular attribute. The primary sets of options available
in this dialog box are:
Tree Inference
Phylogeny Test and Options
To assess the reliability of a phylogenetic tree, MEGA provides two different
types of tests: the Bootstrap test and the Interior branch test. Both of these
tests use the bootstrap re-sampling strategy, so you need to enter the
number of replicates and a starting random seed. For a given data set,
applicable tests and the phylogeny inference method are enabled.
Search Options
This sets the extensiveness of the heuristic search for the Minimum
Evolution (ME) tree. MEGA employs the Close-Neighbor-Interchange (CNI)
algorithm for finding the ME tree. It is a branch swapping method, which
begins with an initial NJ tree.
Include Sites
These are options for handling gaps and missing data, including or excluding codon
positions, and restricting the analysis to labeled sites, if applicable.
Gaps and Missing Data
You may choose to remove all sites containing alignment gaps and missing
information before the calculation begins using Complete-deletion option.
Alternatively, you may choose to retain all such sites initially, excluding them as
necessary using the (Pairwise-deletion option).
Codon Positions
By clicking on the ellipses or the lime square, you may select any combination of
1st, 2nd, 3rd, and non-coding positions for analysis. This option is available only if
the nucleotide sequences contain protein-coding regions and you have selected a
nucleotide-by-nucleotide analysis.
Labeled Sites
This option is available only if there are labels associated with some or all of
the sites in the data. By clicking on the ellipses, you will have the option of
including sites with selected labels. If you chose to include only labeled
sites, then these sites will be first extracted from the data and all other
options mentioned above will be enforced. Note that labels associated with
all three positions in the codon must be included for a full codon in the
analysis.
Substitution Model
In this set of options, you can choose various attributes of the substitution models
for DNA and protein sequences.
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Molecular Evolutionary Genetics Analysis
Model
By clicking on the ellipses to the right of the currently selected model, you may
select a stochastic model for estimating evolutionary distance (click on the lime
square to select this row first). This will reveal a menu containing many different
distance methods and models.
Substitutions to Include
Depending on the distance model or method selected, the evolutionary distance can
be teased into two or more components. By clicking on the drop-down button (first
click on the lime square to select this row), you will be provided with a list of
components relevant to the chosen model.
Transition/Transversion Ratio
This option will be visible if the chosen model requires you to provide a value for
the Transition/Transversion ratio (R).
Pattern among Lineages
This option becomes available if the selected model has formulas that allow the
relaxation of the assumption of homogeneity of substitution patterns among
lineages.
Rates among Sites
This option becomes available if the selected distance model has formulas
that allow rate variation among sites. If you choose gamma-distributed
rates, then the Gamma parameter option becomes visible.
5.34 Maximum Parsimony (MP) Method
5.35 Branch-and-Bound algorithm
The branch-and-bound algorithm is used to find all the MP trees . It guarantees to
find all the MP trees without conducting an exhaustive search. MEGA also
employs the Max-mini branch-and-bound search, which is described in detail in
Kumar et al. (1993) and Nei and Kumar (2000, page 123).
Alignment Gaps and Sites with Missing Information
In MEGA, gap sites are ignored in the MP analysis, but there are two different
ways to treat these sites. One is to delete all of these sites from data analysis. This
option, called the Complete-Deletion option, is generally desirable because
different regions of DNA or amino acid sequences often evolve under different
evolutionary forces. However, if the number of nucleotides (or amino acids)
involved in a gap is small and gaps are distributed more or less randomly, you may
include all such sites and treat them as missing data. Therefore, gaps and missing
data are never used in computing tree lengths in MEGA 4.
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Part IV: Evolutionary Analysis
Consensus Tree
The MP method produces many equally parsimonious trees. Choosing this
command produces a composite tree that is a consensus among all such trees, for
example, either as a strict consensus, in which all conflicting branching patterns
among the trees are resolved by making those nodes multifurcating or as a
Majority-Rule consensus, in which conflicting branching patterns are resolved by
selecting the pattern seen in more than 50% of the trees.
(Details are given in Nei and Kumar [2000], page 130).
Analysis Preferences (Maximum Parsimony)
This dialog box contains four overlapping pages, with each page marked by Tabs running
across the top. You can go to any page by simply clicking on the Tab. Each tab page
organizes a set of logically related options. Information from all the pages is used in the
requested analysis, so it is important that you examine the options selected in each tab
before pressing OK to proceed with analysis.
Phylogeny Test and Options
To assess the reliability of the MP trees, MEGA provides the bootstrap test. You
need to enter the number of replicates and a starting random seed for this test.
Search Options
Use this to select between the branch-and-bound and the heuristic (close-neighbor
interchange) searches. For the branch-and-bound search, an optimized Max-mini
branch-and-bound algorithm is used. While this algorithm is guaranteed to find all
the MP trees, a branch-and-bound search often is too time consuming for more than
15 sequences, although this number varies from data set to data set. Alternatively,
you may use the heuristic search (Close-Neighbor-Interchange)., a branch swapping
method that begins with a given initial tree. You may automatically obtain a set of
initial trees by using the Min-mini algorithm with a given search factor.
Alternatively, you can use the random addition option to produce the initial trees.
Include Sites
This provides options for handling gaps and missing data in the analysis, specifying
inclusion and exclusion of codon positions, and restricting the analysis to only some
types of labeled sites (if applicable).
Gaps and Missing Data
You may choose to remove all sites containing alignment gaps and missinginformation before the parsimony analysis begins using the Complete-deletion
option. Alternatively, you may choose to retain all such sites. In this case, all
missing-information and alignment gap sites are treated as missing data in the
calculation of tree length.
Codon Positions
By clicking on the ellipses (or the lime square), you may select any combination of
1st, 2nd, 3rd, and non-coding positions for analysis. This option is available only if the
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Molecular Evolutionary Genetics Analysis
nucleotide sequences contain protein-coding regions. If they do, you can choose
between the analysis of nucleotide sequences or translated protein sequences. If you
choose the latter, MEGA will translate all protein-coding regions into amino acid
sequences and conduct the protein sequence parsimony analysis.
Labeled Sites
This option is available only if there are labels associated with some or all of the
sites in the data. By clicking on the ellipses, you will have the option of including
sites with selected labels. If you choose to include only labeled sites, then these
sites will be the first extracted from the data and all other options mentioned above
will be enforced. Note that labels associated with all three positions in the codon
must be included for a full codon to be incorporated in the analysis.
Heuristic Search
5.36 Min-mini algorithm
This is a heuristic search algorithm for finding the MP tree, and is somewhat
similar to the branch-and bound search method. However, in this algorithm, many
trees that are unlikely to have a small local tree length are eliminated from the
computation of their L values. Thus while the algorithm speeds up the search for
the MP tree, as compared to the branch-and-bound search, the final tree or trees
may not be the true MP tree(s). The user can specify a search factor to control the
extensiveness of the search and MEGA adds the user specified search factor to the
current local upper bound. Of course, the larger the search factor, the slower the
search, since many more trees will be examined.
(See also Nei & Kumar (2000), pages 122, 125)
Close-Neighbor-Interchange (CNI)
In any method, examining all possible topologies is very time consuming. This
algorithm reduces the time spent searching by first producing a temporary tree,
(e.g., an NJ tree when an ME tree is being sought), and then examining all of the
topologies that are different from this temporary tree by a topological distance of dT
= 2 and 4. If this is repeated many times, and all the topologies previously
examined are avoided, one can usually obtain the tree being sought.
For the MP method, the CNI search can start with a tree generated by the random
addition of sequences. This process can be repeated multiple times to find the MP
tree.
See Nei & Kumar (2000) for details.
5.37 Maximum Composite Likelihood Method
Maximum Composite Likelihood Method
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Part IV: Evolutionary Analysis
Maximum Composite Likelihood (MCL) method is used for estimating evolutionary
distances between all pair of sequences simultaneously, with and without
incorporating rate variation among sites and substitution pattern heterogeneities
among lineages. It can also be used to estimate transition/transversion bias and
nucleotide substitution pattern without requiring a priori knowledge of the
phylogenetic tree.
5.38 Statistical Tests of a Tree Obtained
General Comments on Statistical Tests
There are two different types of methods for testing the reliability of an obtained
tree. One is to test the topological difference between the tree and its closely
related tree by using a certain quantity, for example, the sum of all branch lengths
in the minimum evolution method. This type of test examines the reliability of
every interior branch of the tree, and is generally a conservative test as compared
to other tests included in MEGA.
The other type of test examines the reliability of each interior branch whether or
not it is significantly different from 0. If a particular interior branch is not
significantly different from 0, we cannot exclude the possibility of a trifurcation of
the associated branches or that the other types of bifurcating trees can be generated
by changing the splitting order of the three branches involved. Therefore, in
MEGA we implement the bootstrap procedure for estimating the standard error of
the interior branch and test the deviation of the branch length from 0 (Dopazo
1994).
The third type of test is the bootstrap test, in which the reliability of a given branch
pattern is ascertained by examining the frequency of its occurrence in a large
number of trees, each based on the resampled dataset.
Details of these procedures are given in Nei and Kumar (2000, chapter 9).
Condensed Trees
When several interior branches of a phylogenetic tree have low statistical support
(PC or PB) values, it often is useful to produce a multifurcating tree by assuming
that all interior branches have a branch length equal to 0. We call this
multifurcating tree a condensed tree. In MEGA, condensed trees can be produced
for any level of PC or PB value. For example, if there are several branches with PC
or PB values of less than 50%, a condensed tree with the 50% PC or PB level will
have a multifurcating tree with all its branch lengths reduced to 0.
Since branches of low significance are eliminated to form a condensed tree, this
tree emphasizes the reliable portions of branching patterns. However, this tree has
one drawback. Since some branches are reduced to 0, it is difficult to draw a tree
with proper branch lengths for the remaining portion. Therefore we give our
attention only to the topology so the branch lengths of a condensed tree in MEGA
are not proportional to the number of nucleotide or amino acid substitutions.
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Molecular Evolutionary Genetics Analysis
Note that, although they may look similar, condensed trees are different from the
consensus trees mentioned earlier. A consensus tree is produced from many
equally parsimonious trees, whereas a condensed tree is merely a simplified
version of a tree. A condensed tree can be produced for any type of tree (NJ, ME,
UPGMA, MP, or maximum-likelihood tree).
See also Nei and Kumar (2000) page 175.
Interior Branch Tests
Interior Branch Test of Phylogeny
Phylogeny | Interior Branch Test of Phylogeny
A t-test, which is computed using the bootstrap procedure, is constructed based on the
interior branch length and its standard error and is available only for the NJ and Minimum
Evolution trees. MEGA shows the confidence probability in the Tree Explorer; if this
value is greater than 95% for a given branch, then the inferred length for that branch is
considered significantly positive.
See Nei and Kumar (2000) (chapter 9) for further details.
Neighbor Joining (Construct Phylogeny)
Phylogeny | Construct Phylogeny | Neighbor-Joining…
This command is used to construct a neighbor-joining (NJ) tree (Saitou & Nei 1987). The
NJ method is a simplified version of the minimum evolution (ME) method, which uses
distance measures to correct for multiple hits at the same sites, and chooses a topology
showing the smallest value of the sum of all branches as an estimate of the correct tree.
However, the construction of an ME tree is time-consuming because, in principle, the S
values for all topologies have to be evaluated and the number of possible topologies
(unrooted trees) rapidly increases with the number of taxa.
With the NJ method, the S value is not computed for all or many topologies. The
examination of different topologies is imbedded in the algorithm, so that only one final
tree is produced. This method does not require the assumption of a constant rate of
evolution so it produces an unrooted tree. However, for ease of inspection, MEGA
displays NJ trees in a manner similar to rooted trees. The algorithm of the NJ method is
somewhat complicated and is explained in detail in Nei and Kumar (2000).
For constructing the NJ tree, MEGA may request that you specify the distance estimation
method, subset of sites to include, and whether to conduct a test of the inferred tree
through an Analysis Preferences dialog box.
Bootstrap Tests
Bootstrap Test of Phylogeny
Phylogeny | Bootstrap Test of Phylogeny
One of the most commonly used tests of the reliability of an inferred tree is Felsenstein's
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Part IV: Evolutionary Analysis
(1985) bootstrap test, which is evaluated using Efron's (1982) bootstrap resampling
technique. If there are m sequences, each with n nucleotides (or codons or amino acids), a
phylogenetic tree can be reconstructed using some tree building method. From each
sequence, n nucleotides are randomly chosen with replacements, giving rise to m rows of n
columns each. These now constitute a new set of sequences. A tree is then reconstructed
with these new sequences using the same tree building method as before. Next the
topology of this tree is compared to that of the original tree. Each interior branch of the
original tree that is different from the bootstrap tree the sequences it partitions is given a
score of 0; all other interior branches are given the value 1. This procedure of resampling
the sites and the subsequent tree reconstruction is repeated several hundred times, and the
percentage of times each interior branch is given a value of 1 is noted. This is known as
the bootstrap value. As a general rule, if the bootstrap value for a given interior branch is
95% or higher, then the topology at that branch is considered "correct". See Nei and
Kumar (2000) (chapter 9) for further details.
This test is available for four different methods: Neighbor Joining, Minimum Evolution,
Maximum Parsimony, and UPGMA.
5.39 Handling Missing Data and Alignment Gaps
Alignment Gaps and Sites with Missing Information
Gaps often are inserted during the alignment of homologous regions of sequences and
represent deletions or insertions (indels). They introduce some complications in distance
estimation. Furthermore, sites with missing information sometimes result from
experimental difficulties; they present the same alignment problems as gaps. In the
following discussion, both of these situations are treated in the same way.
In MEGA, there are two ways to treat gaps. One is to delete all of these sites from the data
analysis. This option, called the Complete-Deletion, is generally desirable because
different regions of DNA or amino acid sequences evolve under different evolutionary
forces. The second method is relevant if the number of nucleotides involved in a gap is
small and if the gaps are distributed more or less randomly. In that case it may be possible
to compute a distance for each pair of sequences, ignoring only those gaps that are
involved in the comparison; this option is called Pairwise-Deletion. The following table
illustrates the effect of these options on distance estimation with the following three
sequences:
10
1
20
seq1
A-AC-GGAT-AGGA-ATAAA
seq2
AT-CC?GATAA?GAAAAC-A
seq3
ATTCC-GA?TACGATA-AGA
Total sites = 20.
Here, the alignment gaps are indicated with a hyphen (-) and the missing
information sites are denoted by a question mark (?).
Complete-Deletion and Pairwise-Deletion options
Differences/Compariso
ns
Option
Sequence Data
Compl
t
1.
A
C
GA
A GA A A A
158
(1,
2)
(1,
3)
(2,3)
1/1
0
0/1
0
1/10
Molecular Evolutionary Genetics Analysis
ete
deletio
n
2.
3.
A
A
Pairwi
se
Deletio
n
1.
2.
3.
A-AC-GGAT-AGGA-ATAAA
AT-CC?GATAA?GAAAAC-A
ATTCC-GA?TACGATA-AGA
C
C
GA
GA
A GA A C A
A GA A A A
0
0
2/1
2
3/1
3
3/14
In the above table, the number of compared sites varies with pairwise comparisons in the
Pairwise-Deletion option, but remains the same for pairwise comparisons in the CompleteDeletion option. In this data set, more information can be obtained by using the PairwiseDeletion option. In practice, however, different regions of nucleotide or amino acid
sequences often evolve differently, in which case, the Complete-Deletion option is
preferable.
Include Sites Option
With this command you can set the options for handling gaps and missing data in the
analysis, such as including or excluding codon positions, and restricting the analysis to
only some types of labeled sites, if applicable.
Gaps and Missing Data
You may choose to remove all sites containing alignment gaps and missing information
before the parsimony analysis begins (Complete-deletion option). Alternatively, you may
choose to retain all such sites. In this case, all missing-information and alignment gap
sites are treated as missing data in the calculation of tree length.
Codon Positions
By clicking on the ellipses (revealed by clicking on the lime-colored square), you will be
provided with the option of selecting any combination of 1st, 2nd, 3rd, and non-coding
positions for analysis. This option is available only if the nucleotide sequences contain
protein-coding regions. If it does, you can choose between the analysis of nucleotide
sequences or translated protein sequences. If the latter is chosen, MEGA will translate all
protein-coding regions into amino acid sequences and conduct the protein sequence
parsimony analysis.
Labeled Sites
This option is available only if you have labels associated with some or all of the sites in
the data. By clicking on the ellipses, you will be provided with the option of including
sites with selected labels. If you choose to include only labeled sites, then these sites will
be the first extracted from the data. Then all other options mentioned above will be
enforced. Note that labels associated with all three positions in the codon must be
included for a full codon to be incorporated in the analysis.
5.4 Tests of Selection
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5.41 Synonymous/Nonsynonymous Tests
Large Sample Tests of Selection
One way to test whether positive selection is operating on a gene is to compare the relative
abundance of synonymous and nonsynonymous substitutions that have occurred in the
gene sequences. For a pair of sequences, this is done by first estimating the number of
synonymous substitutions per synonymous site (dS) and the number of nonsynonymous
substitutions per nonsynonymous site (dN), and their variances: Var(dS) and Var(dN),
respectively. With this information, we can test the null hypothesis that H0: dN = dS
using a Z-test:
Z = (dN - dS) / SQRT(Var(dS) + Var(dN))
The level of significance at which the null hypothesis is rejected depends on the alternative
hypothesis (H1) .
H0: dN = dS
H1:
(a)
dN ≠ dS
(test of neutrality).
(b)
dN > dS
(positive selection).
(c)
dN < dS
(purifying selection).
For alternative hypotheses (b) and (c), we use a one-tailed test and for (a) we use a twotailed test. These three tests can be conducted directly for pairs of sequences, overall
sequences, or within groups of sequences. For testing for selection in a pairwise manner,
you can compute the variance of (dN - dS) by using either the analytical formulas or the
bootstrap resampling method.
For data sets containing more than two sequences, you can compute the average number of
synonymous substitutions and the average number of nonsynonymous substitutions to
conduct a Z-test in manner similar to the one mentioned above. The variance of the
difference between these two quantities is estimated by the bootstrap method (Nei and
Kumar [2000], page 56).
Analysis Preferences (Z-test of Selection)
In this dialog box, you can view and select options in the Options Summary. Options are
organized in logical sections. A lime square in the right cell of a row indicates that you
have a choice for that particular attribute. The three primary sets of options available in
this dialog box are:
Analysis
Hypothesis to Test
One way to test whether positive selection is operating on a gene is to compare the
relative abundance of synonymous and nonsynonymous substitutions within the
gene sequences. For a pair of sequences, this is done by first estimating the number
of synonymous substitutions per synonymous site (dS) and the number of
nonsynonymous substitutions per nonsynonymous site (dN), and their variances:
Var(dS) and Var(dN), respectively. With this information, we can test the null
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hypothesis that H0: dN = dS using a Z-test:
Z = (dN - dS) / SQRT(Var(dS) + Var(dN))
The level of significance at which the null hypothesis is rejected depends on the
alternative hypothesis (H1):
H0: dN = dS
H1:
(a)
dN ≠ dS
(test of neutrality).
(d)
dN > dS
(positive selection).
(e)
dN < dS
(purifying selection).
For alternative hypotheses (b) and (c), we use a one-tailed test and for (a) we use a
two-tailed test. These three tests can be conducted directly for pairs of sequences,
overall sequences, or within groups of sequences. For testing for selection in a
pairwise manner, you can compute the variance of (dN - dS) by using either the
analytical formulas or the bootstrap resampling method.
For data sets containing more than two sequences, you can compute the average
number of synonymous substitutions and the average number of nonsynonymous
substitutions to conduct a Z-test in a manner similar to the one mentioned above.
The variance of the difference between these two quantities can be estimated by the
bootstrap method (Nei and Kumar [2000], page 56).
Analysis Scope
Use this option to specify whether to conduct an analysis for sequence
pairs, an overall average, or within sequence groups (if sequence groups
are specified).
Std. Err. Computation by
Depending on the scope of the analysis (pairwise versus other), you may
compute standard errors using analytical formulas or the bootstrap method.
Whenever standard errors are estimated by the bootstrap method, you will
be prompted for the number of bootstrap replicates and a random number
seed.
When the selected test involves the computation of average distance, only
the bootstrap method is available for computing standard errors.
Include Sites
These are options for handling gaps and missing data and restricting the analysis to
labeled sites, if applicable.
Gaps and Missing Data
You may choose to remove all sites containing alignment gaps and missing
information before the calculation begins (Complete-deletion option). Alternatively,
you may choose to retain all such sites initially, excluding them as necessary in the
pairwise distance estimation (Pairwise-deletion option).
Labeled Sites
This option is available only if there are labels associated with some or all of
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the sites in the data. By clicking on the ellipses, you will have the option of
including sites with selected labels. If you chose to include only labeled
sites, they will be first extracted from the data and all of the other options
mentioned above will be enforced. Note that labels associated with all three
positions in the codon must be included for a full codon in the analysis.
Substitution Model
In this set of options, you can choose various attributes of the substitution models
for DNA and protein sequences.
Model
By clicking on the ellipses to the right of the currently selected model, you may
select a stochastic model for estimating evolutionary distance (click on the lime
square to select this row first). This will reveal a menu containing many different
distance methods and models.
Transition/Transversion Ratio
This option will be visible if the chosen model requires you to provide a value for
the Transition/Transversion ratio (R).
Analysis Preferences (Fisher's Exact Test)
When the numbers of codons or the total numbers of synonymous and/or nonsynonymous
substitutions are small, the large sample Z-test is too liberal in rejecting the null
hypothesis. In these cases, tests of selection can be conducted to examine the null
hypothesis of the neutral evolution. Only the Nei-Gojobori and Modified Nei-Gojobori
methods can be used for this test because it requires the direct computation of the numbers
of synonymous and nonsynonymous differences, and the number of synonymous and
nonsynonymous sites. It should be used only when sequences show a small number of
differences. To conduct Fisher’s Exact Test, you need to specify two specific options:
Analysis
Hypothesis to Test
This tests for positive selection (dN > dS) and can only be computed for sequence
pairs.
Include Sites
These options handle gaps and missing data and restrict the analysis to labeled sites,
if applicable.
Gaps and Missing Data
You may choose to remove all sites containing alignment gaps and missing
information before the calculation begins by using the Complete-deletion option.
Alternatively, you may choose to retain all such sites initially, excluding them as
necessary using the Pairwise-deletion option.
Labeled Sites
This option is available only if there are labels associated with some or all of
the sites in the data. By clicking on the ellipses, you will have the option of
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including sites with selected labels. If you chose to include only labeled
sites, then these sites first will be extracted from the data then all other
options mentioned above will be enforced. Note that labels associated with
all three positions in the codon must be included for a full codon in the
analysis.
Substitution Model
In this set of options, you choose various attributes of the substitution models for
DNA and protein sequences.
Model
By clicking on the ellipses to the right of the currently selected model, you may
select a stochastic model for estimating evolutionary distance (click on the lime
square to select this row first). This will reveal a menu containing two different
options: the original or modified Nei & Gojobori methods.
Transition/Transversion Ratio
This option will be visible if the chosen model requires you to provide a value for
the Transition/Transversion ratio (R).
Analysis Preferences (Pattern Homogeneity Analysis)
In this dialog box, you can select and view options in the Options Summary. Options are
organized in logical sections and a lime square on the right cell in a row indicates that you
have a choice for that particular attribute. The two primary sets of options available in this
dialog box are to compute the composition distance, disparity index, or to test the
homogeneity of substitution pattern (Kumar and Gadagkar 2001).
Calculate
Use this to specify whether to compute Composition Distance , Disparity Index, or
to test the homogeneity of evolutionary patterns. If the test is selected, MEGA will
conduct the Monte-Carlo analysis, for which you need to provide the number of
replicates and a starting random seed.
Include Sites
These are options for handling gaps and missing data, including or excluding codon
positions, and restricting the analysis to labeled sites (if applicable).
Gaps and Missing Data
You may choose to remove all sites containing alignment gaps and missing
information before the calculation begins by using the Complete-deletion option.
Alternatively, you may choose to retain all such sites initially, excluding them as
necessary by using the Pairwise-deletion option.
Codon Positions
By clicking on the ellipses or the lime square, you may select any combination of
1st, 2nd, 3rd, and non-coding positions for analysis. This option is available only if
the nucleotide sequences contain protein-coding regions and you have selected a
nucleotide-by-nucleotide analysis. If they do, you also can choose between the
analysis of nucleotide sequences or translated protein sequences. If the latter is
chosen, MEGA will translate all protein-coding regions into amino acid sequences
and conduct the protein sequence analysis.
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Labeled Sites
This option is available only if there are labels associated with some or all of
the sites in the data. By clicking on the ellipses, you will have the option of
including sites with selected labels. If you chose to include only labeled
sites, then these sites first will be extracted from the data and all other
options mentioned above will be enforced. Note that labels associated with
all three positions in the codon must be included for a full codon in the
analysis.
5.42 Other Tests
Tajima's Test of Neutrality
Selection | Tajima’s Test of Neutrality
This conducts Tajima’s test of neutrality (Tajima 1989), which compares the number of
segregating sites per site with the nucleotide diversity. (A site is considered segregating
if, in a comparison of m sequences, there are two or more nucleotides at that site;
nucleotide diversity is defined as the average number of nucleotide differences per site
between two sequences). If all the alleles are selectively neutral, then the product 4Nv
(where N is the effective population size and v is the mutation rate per site) can be
estimated in two ways, and the difference in the estimate obtained provides an indication
of non-neutral evolution. Please see Nei and Kumar (2000) (page 260-261) for further
description.
5.5 Molecular Clock Test
Tajima's Test (Relative Rate)
Phylogeny | Relative Rate Tests | Tajima’s Test
Use this to conduct Tajima’s relative rate test (Tajima 1993), which works in the following
way. Consider three sequences, 1, 2 and 3, and let 3 be the outgroup. Let nijk be the
observed number of sites in which sequences 1, 2 and 3 have nucleotides i, j and k. Under
the molecular clock hypothesis, E(nijk) = E(njik) irrespective of the substitution model
and whether or not the substitution rate varies with the site. If this hypothesis is rejected,
then the molecular clock hypothesis can be rejected for this set of sequences.
In response to this command, you can select the three sequences for conducting Tajima’s
test. For nucleotide sequences, this test offers the flexibility of using only transitions, only
transversions, or both. If the data is protein coding, then you can choose to analyze
translated sequences or any combination of codon positions by clicking on the ‘Data for
Analysis’ button.
See Nei and Kumar (2000) (page 193-196) for further description and an example.
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5.6 Substitution Pattern
5.61 Pattern Menu
Pattern Menu
This menu provides access to the test for examining the substitution pattern homogeneity
between sequences (Kumar and Gadagkar 2001) and computing the two statistics related to
this test (pairwise sequence composition distance and the disparity index) (Kumar and
Gadagkar 2001).
Compute Substitution Pattern
Pattern | Compute Substitution Pattern
After selection, the Analysis Preference window will pop out with all the options.
For Include Sites, you can decide how to deal with Gaps/Missing Data; For
Substitution Model, you can choose Pattern among Lineages and Rates among sites
from the dropdowns. Click Compute button to start the calculation. MEGA caption
expert with full figure legends will present the result. You can save and print the
results from this window.
5.62 Compute Pattern Disparity Index
Pattern | Compute Pattern Disparity Index
Under the menu Pattern, select Compute Pattern Disparity Index, an Analysis
Preferences window will show up with options for how to deal with Gaps/Missing
Data and Condon Positions, click button Compute after selection to start the
calculation. The progress bar will appear to show the progress of calculation. A
Disparity Index window shows the results.
5.63 Compute Composition Distance
Pattern | Compute Composite Distance
The Analysis Preferences window will open, for the option Gaps/Missing Data you
can click the dropdown to choose between "Complete Deletion" and "Pairwise
Deletion". After the clicking Compute button, the Composition Distance window
will pop out with the results.
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5.64 Compute Transition/Transversion Bias
5.65 Pattern | Compute Transition/Transversion Bias ®
Select ComputeTransition/Trasversion Bias from Pattern menu, the Analysis
Preference window will appear. For option Include Sites, you can choose how to
deal with Gaps/Missing Data and Condon Positions. For option Substitution
Model, you can choose how to define "pattern among Lineages" and "Rates among
sites". Click Compute button after selection. MEGA caption expert will present the
result with full table/figure legends.
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6 Part V: Visualizing and Exploring Data and
Results
6.1 Distance Matrix Explorer
6.11 Distance Matrix Explorer
The Distance Matrix Explorer is used to display results from the pairwise distance
calculations. It is an intelligent viewer with the flexibility of altering display modes and
functionalities and for computing within groups, among groups, and overall averages.
This explorer consists of a number of regions as follows:
Menu Bar
File Menu
Display Menu
Average Menu
Help: This button brings up the help file.
Tool Bar
The tool bar provides quick access to a number of menu items.
•
General Utilities
•
Lower-left Triangle button: Click this icon to display pairwise distances in the
lower-left matrix. If standard errors (or other statistics) are shown, they will be
displayed in the upper-right.
•
Upper-right Triangle button: Click this icon to display pairwise distances in the
upper-right matrix. If standard errors (or other statistics) also areshown, they
will be displayed in the lower-left.
•
(A,B): This button is an on-off switch to write or hide the name of the
highlighted taxa pair. The taxa pair is displayed in the status bar below.
•
Distance Display Precision
•
: This decreases the precision of the distance display by one decimal place
with each click of the button.
•
: This increases the precision of the distance display by one decimal place
with each click of the button.
•
Column Sizer: This is a slider that increases or decreases the width of the
columns showing the pairwise distances.
The 2-Dimensional Data Grid
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This grid displays the pairwise distances between taxa (or within groups etc.) in the form
of a lower or upper triangular matrix. The taxa names are the row-headers; the column
headers are numbered from 1 to m, with m being the number of taxa. There is a column
sizer for the row-headers, so that you can increase or decrease the column size to
accommodate the full name of the sequences or groups.
•
Fixed Row: This is the first row in the data grid and displays the column number.
•
Fixed Column: This is the first and leftmost column in the data grid. This column is
always visible even if you scroll past the initial screen. It contains taxa names and an
associated check box. To include or exclude taxa from analysis, you can check or
uncheck this box. In this column, you can drag-and-drop taxa names to sort them.
•
Rest of the Grid: Cells to the right of the first column and below the first row contain
the nucleotides or amino acids of the input data. Note that all cells are drawn in light
color if they contain data corresponding to unselected sequences or genes and domains.
Status bar
The left sub-panel shows the name of the statistic for the currently selected value. In the
next panel, the status bar shows the taxa-pair name for the selected value.
6.12 Average Menu (in Distance Matrix Explorer)
With this menu, you can compute the following average values:
Overall: Computes and displays the overall average.
Within groups: Thisitem is enabled only if at least one group is defined. For each group,
an arithmetic average is computed for all valid pairwise comparisons and the results are
displayed in the Distance Matrix Explorer. All incalculable within-group averages are
shown with an "n/c" in red.
Between Groups: This item is enabled only if at least two groups of taxa are defined. For
each between-group average, an arithmetic average is computed for all valid inter-group
pairwise comparisons and results are displayed in the Distance Matrix Explorer. All
incalculable within-group averages are shown with an "n/c" in red.
Net Between Groups: This item is enabled only if at least two groups of taxa are defined.
It computes net average distances between groups of taxa. This value is given by
dA = dXY – (dX - dY)/2
where dXY is the average distance between groups X and Y, and dX and dY are the mean
within-group distances. You must have at least two groups of taxa with a minimum of
two taxa each for this option to work. All incalculable within-group averages are shown
with a red "n/c".
6.13 Display Menu (in Distance Matrix Explorer)
The display menu consists of four main commands:
•
Show Pair Name: This is a toggle to write or hide the name of the taxa pair highlighted,
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which is displayed in the status bar below.
•
Sort Taxa: This provides a submenu for sorting the order of taxa in one of three ways:
by input order, by taxon name or by group name.
•
Show Names: This is a toggle for displaying or hiding the taxa name.
•
Show Group Names: This is a toggle for displaying or hiding the group name next to
the name of each taxon, when available.
•
Change Font: This brings up the dialog box that allows you to choose the type and
size of the font for displaying the distance values.
6.14 File Menu (in Distance Matrix Explorer)
The file menu consists of three commands:
•
Show Input Data Title: This displays the title of the input data.
•
Show Analysis Description: This displays various options used to calculate the
quantities displayed in the Matrix Explorer.
•
Export/Print Distances: This brings up a dialog box for writing pairwise distances as a
text file, with a choice of several formats.
•
Quit Viewer: This exits the Distance Data Explorer.
6.2 Sequence Data Explorer
6.21 Data Menu
6.22 Display Menu
6.23 Highlight Menu
6.24 Statistics Menu
6.3 Tree Explorer
6.31 Tree Explorer
Phylogeny | Any tree-building option
The Tree Explorer displays the evolutionary tree based on the options used to
compute or display the phylogeny. The main menu of the Tree Explorer has the
following items:
File MenuHC_File_Menu_in_Tree_Explorer
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Image MenuHC_Image_Menu_in_Tree_Explorer
Subtree MenuHC_Subtree_in_Tree_Explorer
View MenuHC_View_Menu_in_Tree_Explorer
Compute MenuHC_Compute_in_Tree_Explorer
6.32 Information Box
The information box in the Tree Explorer lists the various statistical attributes of
the displayed tree with the branch or node highlighted. It usually contains multiple
tabs.
General. This reminds the user of the number of taxa (and groups, if any) and of
the strategy used to deal with gaps and missing data.
Tree. This contains information about the type of tree –rooted/unrooted, and the
sum of branch lengths, SBL, or the treelength. In addition, information about the
total number of trees and the tree number of the current tree is displayed.
Branch. In the Tree Explorer window you may click on a branch or on a node of
the tree. If you click on a branch, this tab displays its location in terms of the two
nodes it connects. (Leaf taxa are numbered in the order in which they appear in the
input data file.) This window also displays the length of the selected branch. If
you click on a node, the internal identification number of that node is displayed.
6.33 File Menu (in Tree Explorer)
This menu has the following options:
Save: This brings up the Save As dialog box and saves all the information currently
held by the Tree Explorer to a file in a binary format. This feature allows you to
retrieve the current Tree Explorer session for tree manipulation and printing.
Export Current Tree: This writes the topology of the current tree in the MEGA
tree format to a specified file. Note that only the branching pattern is stored.
Export All Trees: This writes the topologies of all trees in the MEGA tree format
to a specified file. Note that only the branching pattern is stored.
Show Information: This brings up the Information dialog box.
Print: This brings up the Print dialog box and prints the current tree in the
displayed size; if the displayed tree is larger than the page size, it will be printed on
multiple pages.
Print in a sheet: This brings up the Print dialog box and prints the current tree,
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after restricting the size of the printed tree to one sheet. The current tree also can be
printed using the button on the toolbar.
Printer Setup: This allows the user to setup the printer.
Exit Tree Explorer: This exits the Tree Explorer.
6.34 Image Menu (in Tree Explorer)
The image menu contains three options:
Copy to Clipboard: This copies the tree image to the clipboard, which can also be
done by simultaneously pressing Ctrl and ‘C’ keys. You then can paste the copied
image into any other Windows application (e.g., PowerPoint and Word).
Save as Enhanced metafile: This option saves the image as an enhanced
windows metafile (.EMF). It brings up the Save As dialog box to specify the
filename.
Save as TIFF: This option saves the tree image as Tagged Image File Format
(TIFF) with 400dpi resolution and without LZW compression. TIFF is a popular
raster graphics format widely supported by image-manipulation softwares such
as Adobe Photoshop. Note that one cannot edit each tree part in this format
as in the cases of EMF and PDF, while the graphics quality and
cross-platform compatibility are beter. Also, users should notice that the
file size is much larger than EMF and PDF and it usually becomes tens or
hundreds of mega bytes. It brings up the Save As dialog box to specify the
filename.
Loan Taxon Images: This option automatically associates images to each taxon.
To use it, you will be prompted for the directory where the bitmap images (in BMP
format) reside. For each taxon, the image file must have a BMP extension and the
filename must be identical to the taxon name displayed in the Tree Explorer. All
of the valid images that are found will be retrieved and displayed.
6.35 Subtree Menu (in Tree Explorer)
This menu contains the tree manipulation options Swap, Flip and
Compress/Expand. In addition, by clicking on the corresponding items in the
menu (for which there are tool buttons on the left), you can specify the root of the
tree, and display a subtree (a portion of the tree defined by a given internal branch)
in a separate window.
Choosing ‘Divergence Time’ transforms the cursor to an arrow below which is the
icon associated with the divergence time option. To obtain the evolutionary rate of
a specific lineage, you should point the cursor to that branch and click. On the
other hand, if you are interested in the average evolutionary rate of a cluster of two
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or more taxa, then you should click at the node at the common ancestor of the
cluster. Either way, MEGA brings up the Divergence Time dialog box, which
displays the evolutionary rate information for a given divergence time.
Many of these functionalities are also available through tools in the toolbar on the
left side of the displayed tree.
6.36 Subtree Drawing Options (in Tree Explorer)
This dialog box provides choices options for changing various visual attributes for the
selected subtree. If the Overwrite Downstream option is checked, any subtree drawing
options that have been applied to downstream nodes within the current subtree will be
overwritten.
Property Tab:
Name/Caption: This section allows you to provide an alphanumeric caption for
the selected node.
Node/Subtree Marker: This section provides elements for changing the shape and
color of the selected subtree node marker. If the Apply to Taxon Markers option is
checked, the selected shape and color options will be applied to all taxon markers
contained within the subtree.
Branch Line: This section provides various drawing options that will be applied to
the branch lines of the selected subtree.
Display Tab:
Display Caption: If checked, the node caption, if set within the Property Tab,
will be displayed.
Align Vertically: If checked,?????
Display Bracket: If checked, this item will display a bracket that encompasses the
selected subtree using the configured bracket drawing options.
Display Taxon Names: If checked, the taxon names attributed to the leaf nodes
will be displayed.
Display Node Markers: If checked, any node markers that were configured within
the Property Tab will be displayed.
Display Taxon Markers: If checked, any taxon markers that were configured
within the Property Tab will be displayed.
Compress Subtree: If checked, the selected subtree will be compressed and
rendered as a graphical vector according to the configured drawing options.
Image Tab:
Display Image: If checked, the Tree Explorer will display an image, if loaded, at
the configured position relative to the subtree node caption text.
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6.37 Cutoff Values Tab
In this tab, you can specify a cut-off level for the condensed or consensus trees.
Appropriate options become available depending on the trees displayed.
6.38 Divergence Time Dialog Box
This dialog box allows the user to specify the evolutionary rate for constructing
linearized trees. This can be done by providing the evolutionary rate directly or by
providing the divergence time for the given node.
6.39 View Menu (in Tree Explorer)
This menu brings up several viewing options:
Topology only: This displays the tree in the form of relationships among the taxa,
ignoring the branch lengths.
Root on Midpoint: This roots the tree on the midpoint of the longest path
between two taxa.
Arrange Taxa: This allows you to arrange the taxa in the tree based on the order
of taxa in the input data file or to produce a tree that looks "balanced."
Tree/Branch Style: This allows you to select the display of the tree in one of three
styles: Traditional, Radiation, or Circle. For Traditional, there are three additional
options: Rectangular, Straight or Curved.
Show/Hide: This allows you to display or hide the following information: taxon
label, taxon marker, statistics (e.g., bootstrap values), branch lengths, or scale
bar.
Fonts: This allows you to choose features such as font type and size for
information, including the taxon label, statistics, and scale bar.
Options: This brings up the Option dialog box, which provides control over
various aspects of the tree drawing, including individual branches, the
taxon names, and the scale bar.
6.310 Options dialog box (in Tree Explorer)
Through this dialog box, you can specify various drawing attributes for the tree.
All options are organized in five tabs.
TreeHC_Tree_tab_in_Format_dialog_box
BranchHC_Branch_tab_in_Format_dialog_box
LabelsHC_Taxon_Name_tab_in_Format_dialog_box
ScaleHC_Scale_Bar_tab_in_Format_dialog_box
CutoffCutoff_Values_Tab_in_format_dialog_box
6.311 Tree tab (in Options dialog box)
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This allows you to manipulate aspects of the tree, depending on the style you used
to draw the tree. For instance, if you used the traditional rectangular style, then
you can manipulate the taxon separation distance, branch length, or tree width, in
the number of pixels. This tab also contains a schematic of a tree illustrating these
features.
6.312 Branch tab (in Options dialog box)
This tab has options for the following aspects of the tree:
Line Width. This allows the user to choose the width of the lines.
Display Statistics/Frequency. This presents the options to Hide or Show the
statistics and frequency, to choose the font, or to alter the placement of the
numbers by manipulating the horizontal and vertical positions.
Display Branch Length. This presents the option to Show the branch length or
Hide it if it is shorter than a specified length, to alter the placement of the written
branch lengths, and to choose the number of decimal places for writing the branch
lengths.
6.313 Labels tab (in Options dialog box)
This tab has options for the following:
Display Taxon Names. Presents the option to show (checked) or hide (unchecked)
the label and to choose the font.
Display Markers. Allows you to draw small symbols along with or instead of taxa
names in the tree. Two combo boxes and a list allow you to select the marker
graphics and its color.
6.314 Scale Bar tab (in Options dialog box)
This tab has options:
Line Width. This drop-down menu allows you to choose the width of the line and
the font size used in the scale bar. Show Distance Scale. This allows you to show
or hide the scale bar distance, to enter the unit used and to choose its length and the
interval between tick marks.
Show Time Scale. This presents the option of showing or hiding the divergence
time in the scale bar, and to enter the units used. You also can determine the
interval between two major ticks and two minor ticks. To activate this option the
divergence time for a node or the evolutionary rate must be given.
6.315 Compute Menu (in Tree Explorer)
This performs various tree computations, including Condensed tree, Linearized
tree, and Consensus tree, and allows you to estimate the divergence time for each
node using the molecular clock.
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6.4 Caption Expert
6.41 Creating Data Captions with Caption Expert
MEGA includes a Caption Expert system that provides the ability to generate detailed, publication-quality
captions from analysis results. The Caption Expert system is available for every type of analysis result that can
be generated using MEGA (distance matrix, phylogeny, tests, etc). When invoked, the Caption Expert system
will analyze the properties of the analysis results and provide a caption title followed by a detailed explanation
of the analysis results. The caption text will reveal the properties of the data that underwent the analysis as well
as the assumptions and parameters relevant to the computational methods that were employed. In some cases a
data table will be included in the output. The resulting caption text is displayed in its own window allowing it
to be printed directly, or copied and pasted into external applications such as Microsoft® Word or PowerPoint.
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Appendix
7 Appendix
7.1 Frequently Asked Questions
7.11 Computing statistics on only highlighted sites in Data
Explorer
Go to the Statistics menu in the Sequence Data Explorer, and click on Use highlighted
sites only. Now all statistical quantities computed using the Statistics menu will be based
only on the highlighted sites.
7.12 Finding the number of sites in pairwise comparisons
If you want to find the number of sites between pairs of sequences or the average number
of sites, then go to the Distance menu and select the desired distance type. Then in
Substitutions to Include, select anoption regarding the number of sites.
7.13 Get more information about the codon based Z-test for
selection
The codon based Z-test for selection can be done in two places. First, you can use the
Tests | Codon Based tests of selection | Z-test (large sample) option to find the probability
that the null hypothesis will be rejected, in addition to the actual value of the Z-statistic.
Alternatively, if you want to know the difference between s and n (synonymous and
nonsynonymous substitutions and their variance, you can go to the Distances | Pairwise
menu option and in the distance computation dialog, select an appropriate method (e.g.,
Nei-Gojobori method) and then choose s-n (or n-s depending on your need) from the
Substitutions to include menu. Also, you can choose to compute standard error.
7.14 Menus in MEGA are so short; where are all the options?
Our aim in developing the objectively driven user-interface of MEGA 4 has been a clutterfree work environment that asks the user for information on a need-to-know basis
Although this modular analytical tool looks simple, behind each menu item is a wide range
of useful options and tools that come with enhancements that are designed to reduce the
amount of time needed for mundane non-technical tasks. Consider, for example, the
Sequence Data Explorer. This unique module is hidden away when you don't want it but
is always working behind the scenes. It allows you to view the data in various ways, export
data subsets, and compute many important basic statistical quantities. Another interesting
module is the Genetic Code selector, which allows you to choose the depth at which you
wish to work with a code table. With it you can select a desired code table, add new data
to and edit the existing code table, view the selected code table in a conventional format,
compute the degeneracy for each site in every codon, and compute the number of
potentially synonymous and nonsynonymous sites for each codon. In addition, you can
always find help by checking the help index.
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7.15 Writing only 4-fold degenerate sites to an output file
All sequence data subset facilities are accessible through the Export Data command in the
Sequence Data Explorer. To write 4-fold degenerate sites to a file, highlight the 4-fold
degenerate sites on the screen and then select Export Data. In that command, choose to
write only the highlighted sites. For example, if you select to write only the third codon
positions, all 4-fold degenerate sites found in the third codon positions will be written to
the file.
7.2 Main Menu Items and Dialogs Reference
7.21 Main MEGA Menus
Main MEGA Window
The main window in MEGA contains a menu bar, a toolbar, and a data description window
(DDW). The menu bar may contain two or more menu items depending on whether a data
file is active and on the type of data being analyzed.
Menu Bar
Menus: Description
File menu
Use the File menu commands to open, save, close
data for analysis and for editing text files
Data menu
Use the View menu commands to display the active
data, edit different data attributes, and compute
basic statistical properties.
Distances
menu
Use the Distance menu commands to calculate
evolutionary distances and diversity.
Pattern
menu
Use this menu to conduct tests and compute
statistics regarding the substitution pattern
homogeneity among lineages.
Selection
menu
Use this menu to conduct tests of selection.
Phylogen
y menu
Use the Phylogeny menu commands to calculate
evolutionary trees, test their reliability, and view
saved trees.
Alignment
menu
Use this menu to construct sequence alignments
and explore the world-wide-web.
Help menu
Use the Help menu to access the online help
system, which is displayed in a special help
window.
Toolbar
This contains shortcuts to some frequently used menu commands, such as those in the
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Data menu.
Data Description window
This displays a summary of the currently active data set.
File Data
Open Data
File | Open Data
Choose this command to load a data file for analysis. A dialog box will appear to allow
you to give the data file name. MEGA will first read the data file to check if it contains the
Format command (see MEGA format), which specifies certain attributes of the input data
(e.g., type of data). If MEGA does not find sufficient information in the format command,
it will request the necessary information through an Input Data Format dialog.
If you attempt to open a dataset from a file and MEGA detects inconsistencies or errors in
the format, it will open the file in the text editor, allowing you to make changes in the text
file so that it conforms to the MEGA format.
Once a data file is opened successfully, the Open Data command will be disabled, some of
the file’s basic attributes will be displayed the bottom of the main window. To enable the
Open Data command, close the currently active data using the File | Close Data command.
Open dialog box
Use the open dialog box to load new data into MEGA for analysis.
Property
Description
Look In
Lists the current directory. Use the drop-down list to select a
different drive or directory.
Files
Displays all files in the current directory matching the wildcards
given in File Name or the file type in Files Of Type. You can
display a list of files (default) or you can show details for each file.
File Name
Enter the data file name you want to load or type in the wildcards
to use as filters.
Files of Type
Choose the type of data file you want to open. At present MEGA
allows you to load in MEGA format files only, which should
usually have the .MEG extension.
Up One Level
Click this button to move you directory level up from the current
directory.
Create New
Folder
Click this button to create a new subdirectory in the current
directory.
List
Click this button to view a list of files and directories in the current
directory.
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Details
Click this button to view a list of files and directories along with
time stamp, size, and attribute information.
Export Data
File | Export Data
This command activates the appropriate input data explorer, presents a dialog box
for specifying options and a file for writing the currently active data subset in a
chosen format.
Reopen Data
File | Reopen Data
This reopens a recently closed data file from the submenu, which shows the names of the
five most recently used data files.
Close Data
File | Close Data
This deactivates the currently open data file. Before issuing this command, save any
modifications that you wish to retain by exporting the data through the data explorer (Data
| Data Explorer).
This command is enabled only if a dataset is loaded in MEGA.
Exit
File | Exit
This command closes the currently active data file and all other windows. If you want to
save changes to the data set displayed on the screen, before issuing this command you
must choose File | Export Data and Print or Save. Note that MEGA does not
automatically save changes made to active data to the original data file.
Printer Setup
File | Printer Setup
Choose this command to change the properties of your printer.
File Menu
File Menu
This allows you to perform various important tasks, including activating a data file,
closing a data file, editing text files, and exiting MEGA.
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Data Menu
Data Explorer
Data | Data Explorer
Data Exploreris used to view the currently active data set, calculate its basic statistical
attributes, export it in formats compatible with other programs, and define subsets for
analysis. Depending on the currently active data type, one of the following explorers will
be available:
Data Type
Explorer
DNA, RNA, Protein
sequences
Sequence Data Explorer
Evolutionary divergence
Distance Data Explorer
Include Codon Positions
Data | Select Preferences | Include Codon Positions
Use this menu item to specify the codon positions you would like to include in the
nucleotide sequence analysis. You can include any combination of 1st, 2nd, 3rd positions
and non-coding sites. The specified options are used only if you conduct a nucleotide-bynucleotide site analysis. If relevant, you will be given this choice in the dialog box that
appears in response to a requested analysis (e.g., distance computation or phylogenetic
reconstruction). Thus, you have the flexibility to select or change appropriate options at
the time of the analysis.
Include Labeled Sites
Data | Select Preferences | Include Labeled Sites
Use this to specify whether to include only the labeled sites in the analysis and, if so,
which ones. This option is available only if you have some sites labeled. If relevant, you
also will be given this choice in the dialog box that appears in response to a requested
analysis (e.g., a distance computation or phylogenetic reconstruction). Thus, you have the
flexibility to select or change appropriate options at the time of the analysis.
Handling Gaps and Missing Data
Data | Select Preferences | Handling Gaps and Missing Data
Use this to specify whether to use the Pairwise-Deletion or the Complete- Deletion option
for handling alignment gaps and missing data. You also can specify these options in the
dialog box that appears in response to a requested analysis (e.g., distance computation or
phylogenetic reconstruction). Therefore, you have the flexibility to select or change
appropriate options at the time of the analysis.
Select Preferences
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Data | Select Preferences
This submenu specifies (1) how the alignment gaps and missing data will be handled, (2)
which codon positions will be used, and (3) whether to restrict the analysis to the sites with
selected labels. One or more of these options may be disabled depending on the attributes
of the data set. For instance, the selection of codon positions is not valid when amino acid
sequence data is being analyzed.
These options also are available in the Options dialog box that appears in response to a
requested analysis (e.g., distance computation or phylogenetic reconstruction). Thus, you
have the flexibility to select and change appropriate options at the time of the analysis.
Distances Menu
Distances Menu
Distances Menu
Use this menu to compute: pairwise and average distances between sequences; within,
between, and net average distances among groups; and sequence diversity statistics for data
from multiple populations.
Choose Model
Distances | Choose Model…
Choose this to select a specific model of change for computing distances. The model also
can be chosen or changed in the dialog box that appears when you request an analysis,
such as distance computation or phylogenetic reconstruction.
Compute Pairwise
Distances | Compute Pairwise…
Choose this to compute the distances and standard errors between pairs of taxa. A Select
Distance Options dialog, in which you can choose the desired distance estimation method
and other relevant options, will appear.
Compute Overall Mean
Distances | Compute Overall Mean…
This calculates the mean pairwise distance and standard error for the set of sequences
under study. The overall mean is the arithmetic mean of all individual pairwise distances
between taxa. A Select Distance Options dialog, in which you can choose the desired
distance estimation method and other relevant options, will appear. Before using the
bootstrap method to compute standard error, please read how MEGA implements the
bootstrap method for this purpose.
Compute Within Groups Mean
Distances | Compute Within Groups Means…
This computes the mean pairwise distances within groups of taxa. The within group
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means are arithmetic means of all individual pairwise distances between taxa within a
group. A Select Distance Options dialog, in which you can choose the desired distance
estimation method and other relevant options, appears. You must have at least one group
of taxa, with a minimum of two taxa defined, to utilize this option.
How to define groups of taxa.
Compute Sequence Diversity
Distances | Compute Sequence Diversity
The Sequence Diversity submenu provides four commands for computing the population
and subpopulation diversities that are useful in molecular population genetics studies.
First, you define a group, using a population of sequences. Unlike the generic averages of
within group, between group, and net between group distances calculated using other
commands in the Distances menu, formulas used in the following commands are those
used specifically in population genetics analyses.
The commands are:
Mean Diversity within Subpopulations
In a subpopulation, the mean diversity is defined as
where is the frequency of i-th sequence in the sample
from subpopulation i, and q is the number of different sequences in this
subpopulation.
Mean Diversity for Entire Population
For the entire population, the mean diversity is defined as
, where is the estimate of average frequency of the i-th
allele in the entire population, and q is the number of different sequences in the
entire sample.
Mean Interpopulational Diversity
The estimate of interpopulational diversity is given by
.
Coefficient of Differentiation
The estimate of the proportion of interpopulational diversity is given by
.
Compute Net Between Groups Means
Distances | Compute Net Between Groups Means…
This command computes the net average distances between groups of taxa. The net
average distance between two groups is given by
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dA = dXY – ((dX + dY)/2)
where, dXY is the average distance between groups X and Y, and dX and dY are the mean
within-group distances. A Select Distance Options dialog, in which you can choose the
desired distance estimation method and other relevant options, will appear.
You must have at least two groups of taxa with a minimum of two taxa each for this option
to work.
How to define groups of taxa.
Compute Between Groups Means
Distances | Compute Between Groups Means…
This computes the average distances between groups of taxa. The average distance is the
arithmetic mean of all pairwise distances between two groups in the inter-group
comparisons. A Select Distance Options dialog, in which you can choose the desired
distance estimation method and other relevant options, will appear. You must have at least
two groups of taxa for this option to work.
How to define groups of taxa.
Phylogeny Menu
Phylogeny Menu
Phylogeny Menu
Use the Phylogeny menu to construct phylogenetic trees, infer their reliability using the
bootstrap and interior branch tests, conduct molecular clock tests, and view previously
constructed trees.
Display Saved Tree Session
Phylogeny | Display Saved Tree Session…
Use this command to display a previously saved Tree Explorer session (saved in a
filename with .MTS extension).
Relative Rate Tests
Relative Rate Tests
Phylogeny | Relative Rate Tests
This submenu provides access to a test of the constancy of evolutionary rates between two
sequences or clusters of sequences, using an outgroup sequence.
Construct Phylogeny
Construct Phylogeny
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Phylogeny | Construct Phylogeny
This submenu contains commands for constructing Neighbor Joining, Minimum
Evolution, Maximum Parsimony, and UPGMA trees.
Neighbor-Joining (NJ) Method
This method (Saitou and Nei 1987) is a simplified version of the minimum
evolution (ME) method (Rzhetsky and Nei 1992). The ME method uses distance
measures that correct for multiple hits at the same sites; it chooses a topology
showing the smallest value of the sum of all branches (S) as an estimate of the
correct tree. However, construction of an ME tree is time-consuming because, in
principle, the S values for all topologies must be evaluated. Because the number of
possible topologies (unrooted trees) rapidly increases with the number of taxa, it
becomes very difficult to examine all topologies.
In the case of the NJ method, the S value is not computed for all or many
topologies, but the examination of different topologies is embedded in the
algorithm, so that only one final tree is produced. The algorithm of the NJ method
is somewhat complicated and is explained in detail in Nei and Kumar (2000, page
103).
The NJ method produces an unrooted tree because it does not require the
assumption of a constant rate of evolution. Finding the root requires an outgroup
taxon. In the absence of outgroup taxa, the root is sometimes given at the midpoint
of the longest distance connecting two taxa in the tree, which is referred to as midpoint rooting.
Minimum Evolution (Construct Phylogeny)
Phylogeny | Construct Phylogeny | Minimum Evolution…
This command is used to construct a phylogenetic tree under the minimum evolution
criterion. In this method the sum, S, of all branch length estimates, i.e.,
S = ?bi,
is computed for all plausible topologies, and the topology that has the smallest S value is
chosen as the best tree: the ME tree. This criterion does not require the assumption of
evolutionary rate constancy as needed in the UPGMA analysis. Therefore the inferred
phylogenetic tree is an unrooted tree, even though, for ease of inspection, it is often
displayed in a manner similar to rooted trees.
MEGA employs the Close-Neighbor-Interchange (CNI) algorithm to find the ME tree.
This is a branch swapping method, which begins with a given initial tree. You can ask
MEGA to automatically construct a Neighbor-Joining (NJ) tree and use that as the starting
tree. Alternatively, you can provide your own topology. Note that the final tree produced
after this search is not guaranteed to be the ME tree. These options are available in the ME
Tree Tab of the Analysis Preferences dialog box, which is displayed before the
phylogenetic analysis begins. This dialog box also allows you to specify the distance
estimation method, subset of sites to include, and whether to conduct a test of the inferred
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tree.
Maximum Parsimony
Maximum Parsimony (MP) Method
Maximum parsimony (MP) methods originally were developed for morphological
characters, and there are many different versions (see Nei and Kumar [2000] for a
review). In MEGA, we consider both of these methods for nucleotide and amino
acid sequence data (Eck and Dayhoff 1966; Fitch 1971).
For constructing an MP tree, only sites at which there are at least two different kinds of
nucleotides or amino acids, each represented at least twice, are used (parsimonyinformative sites). Other variable sites are not used for constructing an MP tree, although
they are informative for distance and maximum-likelihood methods.
MEGA estimates MP tree branch lengths by using the average pathway method for
unrooted trees (see Nei and Kumar [2000], page 132).
To search for MP Trees, MEGA provides three different types of searches: the
max-mini branch-and-bound search, min-mini heuristic search, and closeneighbor-interchange heuristic search. Only the branch-and-bound search is
guaranteed to find all the MP trees, but it takes prohibitive amount of time if the
number of sequences is large (>15). For details, please see chapter 7 in Nei and
Kumar (2000)
Maximum Parsimony (Construct Phylogeny)
Phylogeny | Construct Phylogeny | Maximum Parsimony…
This command is used to construct phylogenetic trees under the maximum parsimony
criterion. For a given topology, the sum of the minimum possible substitutions over all
sites is known as the Tree Length. The topology with the minimum tree length is known
as the Maximum Parsimony tree.
The phylogenetic tree(s) inferred using this criterion are unrooted trees, even though, for
ease of inspection, they are often displayed in a manner similar to rooted trees.
MEGA includes the Max-mini branch-and-bound search, which is guaranteed to find all
the MP trees. However, it is often too time consuming for more than 15 sequences. In
those cases, you should use the Close-Neighbor-Interchange (CNI) algorithm to find the
MP tree. CNI is a branch swapping method that begins with a given initial tree. You can
ask MEGA to automatically obtain a set of initial trees by using the Min-mini algorithm
with a given search factor. Alternatively, you can produce the initial trees by providing
your own topology or by using the random addition option. These options are available in
the MP Tree Tab of the Options dialog box and are displayed before the phylogenetic
analysis begins. Note that these CNI branch-swapping procedures may not produce the
best MP trees or all the MP trees.
By default, all nucleotide (or amino acid) changes are weighted equally in MEGA
(standard parsimony). However, for nucleotide sequences, you have the option of
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conducting a transversion parsimony analysis in which only transversional changes are
considered for calculating the tree length. In addition, through the Analysis
Preferences/Options dialog box, you are given options on which subset of sites to include,
and whether to conduct a test of the inferred tree .
UPGMA
UPGMA
This method assumes that the rate of nucleotide or amino acid substitution is the
same for all evolutionary lineages. An interesting aspect of this method is that it
produces a tree that mimics a species tree, with the branch lengths for two OTUs
being the same after their separation. Because of the assumption of a constant rate
of evolution, this method produces a rooted tree, though it is possible to remove the
root for certain purposes. The algorithm for UPGMA is discussed in detail in Nei
and Kumar (2000, page 87).
UPGMA (Construct Phylogeny)
Phylogeny | Construct Phylogeny | UPGMA…
This command is used to construct a UPGMA tree. This tree-making method assumes that
the rate of evolution has remained constant throughout the evolutionary history of the
included taxa. Therefore, it produces a rooted tree.
If your input data is a distance matrix, then using this command makes MEGA proceed
directly to constructing and displaying the UPGMA tree. In all other instances, you will
be asked in an Analysis Preferences dialog box to specify the distance estimation method,
subset of sites to include, and whether to conduct a test of the inferred tree.
Pattern Menu
Selection Menu
Selection Menu
Selection Menu
This menu provides access to codon-based tests of selection as well as to Tajima’s test of
neutrality.
Codon Based Z-Test (large sample)
Selection | Codon Based Z-test (large sample)
One way to test whether positive selection is operating on a gene is to compare the relative
abundance of synonymous and nonsynonymous substitutions that have occurred in the
gene sequences. For a pair of sequences, this is done by first estimating the number of
synonymous substitutions per synonymous site (dS) and the number of nonsynonymous
substitutions per nonsynonymous site (dN), and their variances: Var(dS) and Var(dN),
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respectively. With this information, we can test the null hypothesis that H0: dN = dS
using a Z-test:
Z = (dN - dS) / SQRT(Var(dS) + Var(dN))
The level of significance at which the null hypothesis is rejected depends on the alternative
hypothesis (H1)
H0: dN = dS
H1:
(a)
dN ? dS
(test of neutrality).
(b)
dN > dS
(positive selection).
(c)
dN < dS
(purifying selection).
For alternative hypotheses (b) and (c), we use a one-tailed test and for (a) we use a twotailed test. These three tests can be conducted directly for pairs of sequences, overall
sequences, or within groups of sequences. For testing for selection in a pairwise manner,
you can compute the variance of (dN - dS) by using either the analytical formulas or the
bootstrap resampling method.
For data sets containing more than two sequences, you can compute the average number of
synonymous substitutions and the average number of nonsynonymous substitutions to
conduct a Z-test in a manner similar to the one mentioned above. The variance of the
difference between these two quantities is estimated by the bootstrap method (See Nei and
Kumar (2000) page 55).
Codon Based Fisher's Exact Test
Selection | Codon Based Fisher’s Exact Test
This provides a test of selection based on the comparison of the numbers of synonymous
and nonsynonymous substitutions between sequences. Use this command to conduct a
small sample test of positive selection (Zhang et al. 1997): a one-tailed Fisher’s Exact test.
If the resulting P -value is less than 0.05, then the null hypothesis of neutral evolution
(strictly neutral and purifying selection) is rejected. If the observed number of
synonymous differences per synonymous site (pS) exceeds the number of nonsynonymous
differences per nonsynonymous site (pN) then MEGA sets P = 1 to indicate purifying
selection, rather than positive selection.
See Nei and Kumar (2000) (page 56) for further description and an example.
Alignment Menu
Alignment Menu
Alignment Menu
This menu provides access to options for viewing and building DNA and protein sequence
alignments and for exploring the web based databases (e.g., NCBI Query and BLAST
searches) in the MEGA environment.
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Alignment Explorer/CLUSTAL
Alignment | Alignment Explorer/CLUSTAL
This option displays the Alignment Explorer, which can be used to view and build DNA
and protein sequence alignments and to explore the web based databases (e.g., NCBI
Query and BLAST searches) in the MEGA environment.
Query Databanks
Alignment | Query Databanks
Use this to open the MEGA web-browser to search the NCBI and other web sites for
sequence data.
Show Web Browser
Alignment | Show Web Browser
Use this option to launch the MEGA Web Browser.
View/Edit Sequencer Files
Alignment | View/Edit Sequencer Files
Use this option to view/edit the sequence data in ABI (*.abi and .ab1) and Staden (.scf)
files. The Alignment Explorer provides this option directly.
Help Menu
Help Menu
Help Menu
This menu provides access to the help index as well as the About dialog box, which
provides version information for MEGA.
Index
Help | Index
This command provides access to the help file index and keyword searching facilities.
About
Help | About…
This command will display the About dialog box showing the copyright, authors, and
version information for MEGA.
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7.22 MEGA Dialogs
Input Data Format Dialog
The Input Data Format dialog is displayed if MEGA does not find enough information
about the type of data included in the input file.
Data Type
This displays the list of data types that MEGA is able to analyze. Highlight the current
data type by clicking on it. Depending on the type of data selected, you may need to
provide information about the following additional items.
For Sequence Data
•
Missing Data
Character used to show missing data in the data file; it should be set to a question mark
(?).
•
Alignment Gap
Character used to represent gaps inserted in the multiple sequence alignment; it is set to
a dash (-) by default.
•
Identical Symbol
Character used to represent identity with the first sequence in the data files; it is set to a
dot (.) by default.
For Pairwise Distance Data
•
Missing Data
Character used to show missing data in the data file; it should be set to a question mark
(?).
•
Matrix Format
Choose the lower-left or upper-right distance matrix for the pairwise distance data type.
Note: To avoid having to answer these questions every time you read your data file, save
the data by exporting it in MEGA format.
Setup/Select Taxa & Groups Dialog
This dialog box has two sub-windows (Taxa/Groups and Ungrouped Taxa), a panel bar
between them containing a few buttons, and a command panel, with the lower part
containing the Add, Delete, Close, and Help buttons.
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Taxa/Groups sub-window on the left: It shows all the currently defined taxa and group
names hierarchically. If a taxon has been assigned to a group, it will appear connected to
that group. Groups may be displayed in a collapsed format (indicated by a + mark before
their name). You can click '+' to expand the group to a listing of the taxa contained in it,
and click ‘–‘ to collapse the group to only view the group name. Groups that do not
contain any members do not have this box. Next is a checkbox indicating whether a given
group or taxon will be included in an analysis. Following that is an icon indicating a taxon
(single box) or a group (layer of boxes). Grayed out check boxes are used to indicate that
some of the taxa in a group are selected and others are unselected. You can rearrange the
order of taxa and groups using drag-and-drop. However, note that this order is not
automatically used in the Data Explorer. To enforce this order, use the Sort command in
the Data Explorer.
Ungrouped Taxa Sub-window on the right: This shows the names of all the taxa that do
not belong to any of the groups to facilitate your ability to move taxa into groups. If this
sub-window does not appear on your screen, then hold and drag the lower right corner of
the dialog box to expand its width to unhide it.
Middle Command Panel: This resides between the above-mentioned two sub-windows
and contains a splitter on its right edge. You can grab the splitter and move it to change
the proportion of the space taken by the two sub-windows. In this panel left and right
arrow buttons are used to add or remove taxa from the groups. Clicking the hand-with-apencil icon with a highlighted taxon or group name will allow you to edit that name.
Lower Command Panel: In the lower part of the Select/Edit Taxa/Groups window are
buttons that are used to add and/or delete groups. The ‘+’ and ‘–‘ buttons are also present
on the middle command panel.
Buttons
Description
Add
Creates a new group.
Delete
Deletes the currently selected group.
Any taxa that were assigned to the
group will become freestanding.
Ungroup
Makes all the taxa in the selected
group freestanding, but does not
remove the group from the list.
Close
Closes the dialog box.
Help
Brings up help regarding the dialog
box.
How to perform functions:
Function
Description
Creating a new
group
Click on the Add button. Click on the
highlighted name of the group and type in a
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new name.
Deleting a group
Select the group and click the Delete
button. Any taxa that were assigned to this
group will become freestanding.
Adding taxa to a
group
Drag-and-drop the taxon on the desired
group or select one or more taxa in the
Ungrouped Taxa window and click on the
left arrow button on the middle command
panel.
Removing a
taxon from a
group
Click on the taxon and drag-and-drop it into
a group (or outside all groups). Or, select
the taxon and click on the right arrow button
on the middle command panel.
Include/Exclude
taxa or groups
Click the checkbox next to the group or taxa
name.
Setup/Select Genes & Domains Dialog
Use the Gene & Domain Editor to inspect, define, and select domains, and genes, and
labels for individual sites.
The Genes & Domains dialog consists of two tabs: Define/Edit/Select and Site Labels.
Define/Edit/Select tab
This tab contains a hierarchical listing of gene and domain names with the corresponding
information organized into four columns for amino acid sequences and six columns for
nucleotide sequences.
Gene and domain name listing
Each line in this display contains a small 'expand/contract' box, a checkbox, a gene/domain
icon, and the name of the gene or domain. The 'expand/contract' box allows you to display
or hide the information below a given gene. The checkbox shows if the gene or domain is
currently selected for analysis. All defined genes and domains appear below the
Genes\Domain node in the hierarchy. All domain names are shown with a yellow
background. The Independent node shows the number of Independent sites, which are not
assigned to any domains or genes.
If your input data file does not contain any domains, then MEGA automatically creates a
domain called Data. If you wish to create new domains, you should delete the Data
domain to make all sites independent. Remember that only independent sites can be
assigned to domains, and sites cannot be assigned to multiple domains. Genes are simply
collections of domains, and thus gene boundaries are decided based on the domains
contained in them. The MEGA gene and domain organizer is flexible and is designed to
enable you to specify genes and domains as they appear in a genome. For instance, a
sequence may contain one or more genes, each of which may contain one or more
domains. In between genes, there may be intergenic domains. In addition, within or
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between genes or domains, there may be sites that are not members of any domain.
At the bottom of this tab, you will find a toolbar with many drop-down menu buttons,
which can be used to Add/Insert new genes or domains. The add and insert operations
differ in the following way. If you add a gene or domain, then the new gene or domain
will be added at the end of the list to which the currently focused gene or domain belongs.
If you insert a gene (or domain), it will be inserted by shifting all the following genes or
domains down. Add and Insert commands are context sensitive.
You can rearrange the relative position of genes and domains by drag-and-drop operations.
Inspecting/modifying attributes of genes and domains
When you start, all genes and domains are shown. Click on the ‘+’ in the expand/contract
box to expand the listing for each gene to its domains. Click on the ‘-‘ to collapse to the
gene. To select and deselect genes or domains from analysis, click in the corresponding
checkbox. When a gene is selected but some domains within the gene are not, the
checkbox for the gene will be grayed. If you deselect a gene, all domains within that gene
are automatically deselected.
On the right side of the gene and domain hierarchy, you will find at least four columns of
information for each domain and gene. All information shown for genes is computed
based on the domains contained.
The first two columns show the site number in the sequence where the domain begins
(From column) and where it ends (To column). The total number of sites shown next to
the To column indicates the total number of sites automatically computed, based on the
range of information given in the previous two columns. A question mark (?) shows that
the domain exists but that the range of sites is not yet specified.
To specify or change sites that belong to a given domain, click on the domain name. The
corresponding rows in the From and the To columns contain a button with three dots
(ellipses). To change the start site, click on the ellipses in the From column. This will
bring up a small Site Picker dialog box with which you can highlight the desired site and
click OK. In this viewer, you will see that sites have different background colors. A white
background marks independent sites, a red background indicates that the site is used by
another domain, and a yellow background shows that the current site belongs to the
domain being edited. To cancel any changes, click on Cancel in the Site Picker dialog
box.
For nucleotide sequences, two additional columns are found in the Define/Edit/Select tab:
the Coding? column and the Codon Start column. A check-mark in the Coding? column
shows that a given domain is protein coding. If it is checked, then the next column allows
you to specify whether the first site in the domain is in the first, second, or the third codon
position.
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Site Labels Tab
This tab displays sequences and allows you to label individual sites. To do this, change
the default underscore (_) in the topmost line to the label of choice and give it a light green
background. The site number will be displayed below in a window, next to which is
shown the name of the domain, along with gene, name. Labeled sites can be selected or
deselected for analysis.
To change or give a label to a site, click on the site and type in the character you wish to
mark it with. You can use the left and right arrow buttons on the keyboard to move to and
then label adjacent sites. To change a label, simply overtype it. To remove a label, use the
spacebar to type a space.
Example
Imagine an alignment consisting of a genomic sequence, including a gene and its upstream
and downstream regions. You can define each intron and exon as a domain, and then
define the overall gene, assigning the exons and introns to that gene. The upstream and
downstream regions also can be defined as domains, or possibly multiple domains,
depending on the analysis you wish to perform. These domains do not have to be assigned
to any gene. Furthermore, some sites may be left unassigned, as independent sites. These
can be scattered throughout the sequence and can be included or excluded from analysis as
a group. If you have a complicated patterns of sites you wish to analyze as groups, and the
domaingene approach is unsuitable, you should assign a category to these sites, which can
be specified in addition to the groups and domains.
Select Genetic Code Table Dialog
This dialog selects the desired genetic code, and edits and displays the properties of the
genetic codes. At present only one genetic code can be selected in MEGA at any given
time; it is used for all coding regions in all sequences in the data set.
To select a genetic code, click in the square box to its left.
You can also highlight any genetic code by clicking on the text.
You can then use the following buttons found along the top of the dialog box:
Button
Description
Add
Creates a new genetic code table. A code table editor will be shown with the
genetic code of the currently highlighted code table loaded.
Delete
Removes the highlighted genetic code from the list. Note that the standard
genetic code cannot be deleted.
Edit
Modifies the highlighted genetic code or its name. The code table editor will
be invoked for editing the genetic code.
View
Displays the highlighted genetic code in a printable format.
Statistic
s
Displays the number of synonymous and non-synonymous sites for the
codons of the highlighted genetic code following the Nei-Gojobori (1986)
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s
method. The degeneracy values for the first, second, and third codon
positions are displayed following Li et al. (1985).
7.3 Error Messages
7.31 Blank Names Are Not Permitted
As this error message suggests, you cannot leave the name of a sequence, taxa, domain, or
gene blank.
7.32 Data File Parsing Error
An error occurred while parsing the input data file. Pay close attention to the message
provided, then look for the error that occurred just prior to the event indicated in the file.
7.33 Dayhoff/JTT Distance Could Not Be Computed
The Dayhoff/JTT matrix-based correction could not be applied for one or more pairs of
sequences. If you wish to know which pair(s), use the Distances|Pairwise option. They
will be shown in the Distance Matrix Dialog with a red n/c (not computable).
7.34 Domains Cannot Overlap
Any given site can belong to only one domain, at most. If you would like to assign a site
or range of sites belonging to one domain to a second domain, you must first change or
delete the definition of the first domain.
7.35 Equal Input Correction Failed
This error message means that, the Equal Input Model-based correction could not be
applied for the amino acid distances estimation. If you wish to know which pair(s) of
sequences has this problem, use the Distances|Pairwise option. All such pairs will be
shown in the Distance Matrix Dialog with a red n/c (not computable).
7.36 Fisher's Exact Test Has Failed
Fisher's exact test uses estimates of the number of synonymous sites (S), the number of
nonsynonymous sites (N), the number of synonymous differences (Sd), and the number of
nonsynonymous differences (Nd). It fails for a number of reasons. If thenumbers are very
large, some mathematical functions may not be able to handle them, although we have
tried to avoid this by using logarithms of factorials. To diagnose the problem, compute S,
N, Sd, and Nd using the Distances|Pairwise option four times. If you still cannot find the
problem, please contact us
7.37 Gamma Distance Failed Because p > 0.99
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Molecular Evolutionary Genetics Analysis
For amino acid distance estimation, if the proportion of amino acids between two
sequences that are different has exceeded 99%, the gamma distance cannot be calculated.
To know which pair(s) of sequences has this problem, use the Distances|Pairwise option.
All such pairs will be shown in the Distance Matrix Dialog with a red n/c.
7.38 Gene Names Must Be Unique
MEGA requires that all gene names in a genome be unique, although, for convenience,
many domains can have the same name. For example, you may want to give the name
Exon-1 to the first exon in all genes.
7.39 Inapplicable Computation Requested
You have requested a computation that is not allowed or is unavailable for the currently
active dataset. If you think that this is in error, then please report this potential software
bug to us.
7.310 Incorrect Command Used
The selected command or option is not valid here. Please look at the brief description
provided in the error message window to determine the nature of the problem.
7.311 Invalid special symbol in molecular sequences
Unique ASCII characters, except letters and '*', can be used as special symbols for
alignment gaps, missing data, and identical sites. Frequently used symbols for identical
sites, alignment gaps, and missing data are '.', '-', and '?', respectively. This error message
means that you have attempted to use the same symbols for two or more of these types of
sites, or a chosen symbol is not appropriate. For example, do not use N (the ambiguous site
symbol for DNA/RNA sequences), or X (the ambiguous site symbol for protein
sequences) because they are already available as the IUPAC symbols for molecular
sequences.
7.312 Jukes-Cantor Distance Failed
The Jukes-Cantor correction is used to calculate nucleotide distances and synonymous and
nonsynonymous substitution distances. If the proportion of sites that are different
(nucleotides, synonymous, or nonsynonymous) is greater than or equal to 75%, the JukesCantor correction cannot be applied. If you see this error message, then this has happened
for one or more pairs in your data. If you wish to know which pair(s), use the
Distances|Pairwise option. All such pairs will be shown in the Distance Matrix Dialog
with a red n/c.
7.313 Kimura Distance Failed
The Kimura (1980) distance correction is used in a number of operations, including
calculating nucleotide distances and synonymous and nonsynonymous substitution
distances. These formulas cannot be applied if the argument in the logarithm approaches
zero or becomes negative. If you see this error message, then this has happened for one or
more pairs in your data. If you wish to know which pair(s), use the Distances|Pairwise
option. All such pairs will be shown in the Distance Matrix Dialog with a red n/c.
7.314 LogDet Distance Could Not Be Computed
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The formula used for calculating distances contains many log terms. If some of their
arguments approach zero too closely or become negative the LogDet correction cannot be
applied. If you wish to know which pair(s) of sequences has this problem, use the
Distances|Pairwise option. All such pairs will be shown in the Distance Matrix Dialog
with a red n/c (not computable).
7.315 Missing data or invalid distances in the matrix
The selected set of taxa contains one or more pairs for which the evolutionary distance is
either invalid or not available. Please inspect the distance data in the Data Explorer to
identify those pairs and remove one or more taxa, as needed.
7.316 No Common Sites
For the sequences and data subset options selected, MEGA found zero common sites. If
you selected the complete deletion option then you might achieve better results using the
pairwise deletion option, as complete deletion removes all sites containing a gap in any
part of the alignment. If you selected the pairwise deletion option then MEGA was unable
to calculate the distance between one or several of the sequence pairs in the alignment. To
identify such pairs compute a pairwise distance matrix using the p-distance method and
look for the word "n/c" in place of the pairwise distance value.
7.317 Not Enough Groups Selected
The currently active dataset or subset does not contain enough groups to conduct the
desired analysis. Please define or select more groups using the Setup Taxa and Groups
Dialog.
7.318 Not Enough Taxa Selected
The currently active dataset or subset does not contain enough sequences or taxa to
conduct the desired analysis. Please add or select more sequences.
7.319 Not Yet Implemented
The task you requested was not activated. This function either was not be available in
your release of MEGA or needs to be activated by us. Please contact the authors and report
this software bug at your earliest convenience.
7.320 p distance is found to be > 1
This peculiar situation can occur in the computation of the proportion of synonymous (or
nonsynonymous) substitutions per site, especially when the number of included codons is
small. If you wish to know which pair(s) of sequences has this problem, please use the
Distances|Pairwise option. All such pairs will be shown in the Distance Matrix Dialog
with a red n/c.
The Kimura (1980) distance correction is used in a number of operations, including
calculating nucleotide distances and synonymous and nonsynonymous substitution
distances. These formulas cannot be applied if the argument in the logarithm approaches
zero or becomes negative. If you see this error message, then this has happened for one or
more pairs in your data. If you wish to know which pair(s), use the Distances|Pairwise
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Molecular Evolutionary Genetics Analysis
option. All such pairs will be shown in the Distance Matrix Dialog with a red n/c.
7.321 Poisson Correction Failed because p > 0.99
For an amino acid estimation of distances, the proportion of amino acids that differ
between two sequences has exceeded 99% and the Poisson correction distance formula
cannot be applied. If you wish to know which pair(s) of sequences has this problem, use
the Distances|Pairwise option. All such pairs will be shown in the Distance Matrix
Dialog with a red n/c (not computable).
7.322 Tajima-Nei Distance Could Not Be Computed
For one or more pairs of sequences, the Tajima-Nei correction could not be applied, which
usually occurs if the argument in the log term of the formula becomes too close to zero. If
you wish to know which pair(s) of sequences has this problem, use the Distances|Pairwise
option. All such pairs will be shown in the Distance Matrix Dialog with a red n/c (not
computable).
7.323 Tamura (1992) Distance Could Not Be Computed
For one or more pairs of sequences, the Tajima-Nei correction could not be applied. This
usually occurs if the argument in the log term of the formula becomes too close to zero or
if it is negative, or if the G+C-content is 0% or 100%. If you wish to know which pair(s)
of sequences has this problem, use the Distances|Pairwise option. All such pairs will be
shown in the Distance Matrix Dialog with a red n/c (not computable).
7.324 Tamura-Nei Distance Could Not Be Computed
The Tamura-Nei distance formula contains many log terms. If some of their arguments
approach zero too closely or become negative, the Tamura-Nei model correction cannot be
applied. If you wish to know which pair(s) of sequences has this problem, use the
Distances|Pairwise option. All such pairs will be shown in the Distance Matrix Dialog
with a red n/c (not computable).
7.325 Unexpected Error
While carrying out the requested task, an unexpected error has occurred in MEGA. Please
contact the authors and report this software bug as soon as possible. We will try to solve
the problem at the earliest possible time.
7.326 User Stopped Computation
You have aborted the current process by pressing the Stop process button on the progress
indicator.
7.4 Glossary
7.41 ABI File Format
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The ABI File Format is a binary file that is produced by ABI sequencer software.
This data file, referred to as a "trace file" is viewable in MEGA’s Trace File Editor,
which is part of the Alignment Explorer.
7.42 Alignment Gaps
Phylogenetic analysis on two or more DNA or amino acid sequences requires that
the sequences be aligned so that the substitutions can be accurately enumerated.
During alignment, gaps must be introduced in sequences that have undergone
deletions or insertions. These gaps are known as alignment gaps or indels.
7.43 Alignment session
When working in MEGA’s Alignment Explorer you can choose to save the current
state of all data and settings in the alignment explorer to a file so you can archive
your work, or save it to resume editing in the future. An alignment session is a
binary file format that is saved with the .MAS file extension.
7.44 Bifurcating Tree
A bifurcating tree is one in which each ancestral lineage gives rise to exactly two
descendent lineages. A tree with only bifurcating nodes is called a bifurcating tree.
7.45 Branch
A branch is a line connecting either two internal nodes to each other or an external
node to an internal node in a phylogenetic tree. The length of a branch denotes the
genetic distance (e.g., number of substitutions per unit time) between the two taxa
it connects.
7.46 ClustalW
nClustalW is a general purpose multiple sequence alignment program for DNA or
proteins. You can learn more about ClustalW by visiting its website
(http://www.ebi.ac.uk/clustalw/).
7.47 Codon
A codon is triplet of nucleotides that codes for a specific amino acid.
7.48 Codon Usage
There are 64 (43) possible codons that code for 20 amino acids (and stop signals) so one
amino acid may be encoded by several codons (e.g., serine is encoded by six codons in
nuclear genes). It is therefore interesting to know the codon usage for each amino acid. In
MEGA, the numbers of the 64 codons used in a gene can be computed either for one
specific sequence or for all examined sequences. In addition to the codon frequencies,
MEGA also writes the Sharp et al. (1986) relative synonymous codon usage (RSCU)
statistic (see Nei and Kumar 2000, page 11).
7.49 Complete-Deletion Option
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Molecular Evolutionary Genetics Analysis
In the complete-deletion option, sites containing missing data or alignment gaps
are removed before the analysis begins. This is in contrast to the pairwise-deletion
option in which sites are removed during the analysis as the need arises (e.g.,
pairwise distance computation).
7.410 Composition Distance
Composition distance is a measure of the difference in nucleotide (or amino acid)
composition for a given pair of sequences. It is one half the sum of squared
difference in counts of bases (or residues). MEGA 4 computes and presents the
Composition Distance per site, which is given by the total composition distance
between two sequences divided by the number of positions compared, excluding
gaps and missing data.
7.411 Compress/Uncompress
This command changes the cursor to the 'Compress/Uncompress' icon. If you click
on an interior branch, MEGA will prompt you to give a name to the group that will
be formed. It then will compress all the lineages defined by this branch into a solid
elongated triangle whose thickness is proportional to the number of taxa
condensed. Clicking on the branch again will uncompress it.
The cursor may be reverted to the arrow by clicking on the arrow icon on the left
hand side of the Tree Explorer.
7.412 Condensed Tree
When interior branches in a phylogenetic tree do not have statistically significant
lengths, choosing this command condenses the tree into a topology in which each
branch with less than the desired statistical significance is collapsed.
7.413 Constant Site
A site containing the same nucleotide or amino acid in all sequences is referred to
as a constant site. MEGA identifies a site as a constant site only if at least two
sequences contain unambiguous nucleotides or amino acids.
7.414 Degeneracy
0-fold degenerate sites are those at which all changes are nonsynonymous.
2-fold degenerate sites are those at which one out of three changes is synonymous.
(All sites at which two out of three changes are synonymous also are included in
this category.)
4-fold degenerate sites are those at which all changes are synonymous.
7.415 Disparity Index
Disparity Index measures the observed difference in substitution patterns for a pair
of sequences. It works by comparing the nucleotide (or amino acid) frequencies in
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Appendix
given pair of sequences and using the number of observed differences between
sequences. MEGA 4 computes and presents the Disparity Index per site, which is
given by the total disparity index between two sequences divided by the number of
positions compared, excluding gaps and missing data. It is more powerful than a
chi-square test of the equality of base frequencies between sequences.
7.416 Domains
A domain is a continuous block of sites in a sequence alignment. A domain can be
free-standing or assigned to genes and protein-coding (e.g., exons) or non-coding
(e.g., introns). Domains can be defined in the input data, and can be defined and
edited in the Setup Genes Domains dialog.
7.417 Exon
A protein-coding gene typically consists of multiple coding regions, known as
exons, interspersed with non-coding DNA (introns)
7.418 Extant Taxa
The taxa whose sequences, other genetic information or morphological characters,
etc. are being used for a phylogenetic analysis are known as extant taxa,
irrespective of whether the individuals or species to whom the sequences and other
information belong are extant or extinct.
7.419 Flip
This command changes the cursor to the 'Flip' icon. Then, if you click on an
interior branch, MEGA reverses the order of the lineages defined by this branch.
The cursor will revert to the arrow if you click on the arrow icon on the left hand
side of the Tree Explorer.
7.420 Format command
A format command in a data file begins with !Format and contains at least the
data type included in the file.
7.421 Gamma parameter
According to the gamma distribution, the substitution rate often varies from site to
site within a sequence. The shape of this distribution is determined by a value
known as the gamma parameter, which is also known as the shape parameter.
7.422 Gene
A gene is a collection of domains. The domains included in a gene need not be
consecutive or of the same type. Genes and domains can be defined in the input
data, and can be defined and edited in the Setup Genes and Domains dialog. Genes
can be selected or unselected from an analysis. When a gene is unselected, all its
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Molecular Evolutionary Genetics Analysis
domains are automatically unselected. However, a gene can be selected, with some
of its domains unselected.
7.423 Genetic Codes
A genetic code table specifies the amino acid residues encoded by the various
codons. Vertebrate mitochondria, Drosophila mitochondria, and yeast
mitochondria all have their own genetic code tables, which are slightly different
from the most common table, the Standard Genetic Code Table.
7.424 Indels
Phylogenetic analysis on two or more DNA or amino acid sequences requires that
the sequences be aligned so that the substitutions can be accurately enumerated.
During the alignment, gaps must be introduced in sequences that have undergone
deletions or insertions. These gaps are known as alignment gaps, or indels.
7.425 Independent Sites
In a sequence alignment, all sites that have not been assigned to any gene or
domain are classified as independent.
7.426 Inferred Tree
A tree reconstructed from the observed sequence or other appropriate data using
any tree-making method (such as UPGMA, NJ, ME, or MP) is known as an
inferred or reconstructed tree.
7.427 Intron
Introns are the non-coding segments of DNA in a gene that are interspersed among
the exons.
7.428 Maximum Composite Likelihood
In general, a composite likelihood is defined as a sum of log-likelihoods for related
estimates. In MEGA4, the maximum composite likelihood is used for describing
the sum of log-likelihoods for all pairwise distances in a distance matrix (Tamura
et al. 2004) estimated by using the Tamura-Nei (1993) model (see related TamuraNei distance). Further information is in the Maximum Composite Likelihood
Method.
7.429 Max-mini branch-and-bound search
This is an algorithm for searching for the MP tree using the branch-and bound
search method. See Nei & Kumar (2000) for details.
7.430 Maximum Parsimony Principle
For any given topology, the sum of the minimum possible substitutions over all
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sites is known as the tree length for that topology. The topology with the minimum
tree length is known as the Maximum Parsimony tree.
7.431 Mid-point rooting
In the mid-point rooting method, the root of an unrooted tree is placed at the midpoint of the longest distance between two taxa in a tree.
7.432 Monophyletic
A cluster of taxa that shared a common ancestor comparatively recently in the
evolutionary history of a phylogenetic tree is monophyletic. The term reflects the
close relationship of the taxa with each other.
7.433 mRNA
Protein-coding genes are first transcribed into messenger RNAs (mRNA), which
are, in turn, translated into amino acid sequences to make proteins.
7.434 NCBI
jAn acronym that stands for "National Center for
Biotechnology Information". NCBI is a federally funded
resource for molecular biology information. NCBI creates
databases, conducts research in computational biology,
develops software and tools for analyzing genome data,
and disseminates biomedical information. You can find
out more about NCBI by visiting the NCBI website
(http://www.ncbi.nlm.nih.gov).
7.435 Newick Format
NEWICK is a simple format used to write out trees in a text file. While this is a
hard-to-read format for humans, it is very useful for exchanging trees between
different types of software. An example of the contents of a NEWICK format tree
file is given below (note that semi-colon is needed to end the tree). Further
information on this format can be found at Joe Felsenstein’s website.
((raccoon, bear),((sea_lion,seal),((monkey,cat), weasel)),dog);
The above tree with branch lengths will look as follows:
((raccoon:19.19959,bear:6.80041):0.84600,((sea_lion:11.99700,
seal:12.00300):7.52973,((monkey:100.85930,cat:47.14069):20.59201,
weasel:18.87953):2.09460):3.87382,dog:25.46154);
If you wish to specify bootstrap values then they could appear
before the branch lengths (e.g., in .DND files produced by CLUSTAL)
or after the branch lengths (e.g., in .PHB files produced by
CLUSTAL). In these cases, the format might look like:
((raccoon:19.19959,bear:6.80041)50:0.84600,((sea_lion:11.99700,
seal:12.00300)100:7.52973,((monkey:100.85930,cat:47.14069)80:20.59
201, weasel:18.87953)75:2.09460)50:3.87382,dog:25.46154);
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Molecular Evolutionary Genetics Analysis
or
((raccoon:19.19959,bear:6.80041):0.84600[50],((sea_lion:11.99700,
seal:12.00300):7.52973[100],((monkey:100.85930,cat:47.14069):20.59201[80],
weasel:18.87953):2.09460[75]):3.87382[50],dog:25.46154);
7.436 Node
A node in a phylogenetic tree represents a taxon, the external or terminal nodes
represent the extant taxa and the internal nodes represent the ancestral taxa.
7.437 Nonsynonymous change
A nucleotide change is nonsynonymous if it changes the amino acid encoded by
the original codon. A nucleotide site in which one or more changes are
nonsynonymous is referred to as a nonsynonymous site. If only one of three
possible nucleotide changes at that site is nonsynonymous, then the site is 1/3
nonsynonymous. If two of three nucleotide changes are nonsynonymous, then the
site is 2/3 nonsynonymous. And, if all three possible nucleotide changes are
nonsynonymous, then the site is completely nonsynonymous.
7.438 Nucleotide Pair Frequencies
When two nucleotide sequences are compared, the frequencies of 10 or 16 different types
of nucleotide pairs can be computed. In MEGA, these frequencies are presented in a text
file.
7.439 OLS branch length estimates
The ordinary least squares estimate of a branch length (b) is given by
where dij is the pairwise distance between sequences i and j. The coefficients wij’s
depend on whether the branch under consideration is internal or external.
Coefficients wij’s for an internal branch
where, mA, mB, mC, and mC are the numbers of sequences in clusters A, B, C, and
D, respectively.
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Appendix
Coefficients wij’s for an external branch
where, mA and mB are the numbers of sequences in clusters A and B.
7.440 Orthologous Genes
Two genes are said to be orthologous if they are the result of a speciation event.
7.441 Outgroup
An outgroup is a sequence (or set of sequences) that is known to be a sister taxa to
all other sequences in the dataset.
7.442 Pairwise-deletion option
In the pairwise-deletion option, sites containing missing data or alignment gaps are
removed from the analysis as the need arises (e.g., pairwise distance computation).
This is in contrast to the complete-deletion option in which all such sites are
removed prior to the analysis.
7.443 Parsimony-informative site
A site is parsimony-informative if it contains at least two types of nucleotides (or
amino acids), and at least two of them occur with a minimum frequency of two.
7.444 Polypeptide
A polypeptide is a chain of many amino acids.
7.445 Positive selection
At the DNA sequence level, positive selection refers to selection in favor of
nonsynonymous substitutions. In this case, the evolutionary distance based on
nonsynonymous substitutions is expected to be greater than synonymous
substitutions.
7.446 Protein parsimony
A Maximum Parsimony analysis on protein sequences is known as protein
parsimony.
7.447 Purifying selection
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Molecular Evolutionary Genetics Analysis
Purifying selection refers to selection against nonsynonymous substitutions at the
DNA level. In this case, the evolutionary distance based on synonymous
substitutions is expected to be greater than the distance based on nonsynonymous
substitutions.
7.448 Purines
The nucleotides adenine (A) and guanine (G) are known as purines.
7.449 Pyrimidines
The nucleotides cytosine (C) and thymine (T) are known as pyrimidines.
7.450 Random addition trees
This refers to the generation of random initial trees for a heuristic search to find
MP trees. In this case, a tree is generated by randomly selecting a sequence and
adding it to the growing tree on a randomly-selected branch.
7.451 Rooted Tree
A rooted tree is one in which the root of the phylogenetic tree is determined by
using the mid-point rooting or outgroups sequences.
7.452 RSCU
Many amino acids are coded by more than one codon; thus multiple codons for a
given amino acid are synonymous. However, many genes display a non-random
usage of synonymous codons for specific amino acids. A measure of the extent of
this non-randomness is given by the Relative Synonymous Codon Usage (RSCU)
(Sharp et al. 1986).
The RSCU for a particular codon (i) is given by
RSCUi = Xi / ∑∑ XI /n
where Xi is the number of times the ith codon has been used for a given amino acid,
and n is the number of synonymous codons for that amino acid.
7.453 Singleton Sites
A singleton site contains at least two types of nucleotides (or amino acids) with, at
most, one occurring multiple times. MEGA identifies a site as a singleton site if at
least three sequences contain unambiguous nucleotides or amino acids.
7.454 Staden
The Staden file format is used to store data from DNA sequencing instruments.
Each file contains the data for a single reading and includes the called sequence as
well as additional data obtained from the reading. This file format was first
described in
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Appendix
Dear, S and Staden, R. "A Standard file format for data from DNA sequencing
instruments", DNA Sequence 3, 107-110, (1992)
MEGA is able to display the contents of a Staden-formatted trace file using
MEGA’s Trace File Editor, which is part of the Alignment Explorer.
7.455 Statements in input files
All statements in MEGA files start with an exclamation mark (!) and end with a
semicolon (;). They are useful in specifying various attributes of the data and the
data file. There are three common statements for all types of data: Title,
Format, and Description. There also are other statements that can be used in
MEGA files, depending on the type of data being analyzed.
7.456 Swap
This command changes the cursor to the 'Flip' icon. Then, you click on an interior
branch, MEGA swaps the two subtrees defined by this branch. If each of the
subtrees is an individual taxon, then Swap is the same as Flip.
The cursor will revert to the arrow if you click on the arrow icon on the left-hand
side of the Tree Explorer.
7.457 Synonymous change
A nucleotide change is synonymous if it does not cause the codon to code for a
different amino acid. A nucleotide site in which one or more changes is
synonymous is referred to as a synonymous site. If only one of three possible
nucleotide changes at that site is synonymous, then the site is 1/3 synonymous. If
two of three nucleotide changes are synonymous, then the site is 2/3 synonymous
and 1/3 nonsynonymous. And, if all three possible nucleotide changes are
synonymous, then the site is completely synonymous.
7.458 Taxa
A taxon is the individual unit whose evolutionary relationship is being
investigated. Depending on the study, "taxa" may refer to species, populations,
individuals, or sequences within an individual.
7.459 Topological distance
The topological distance quantifies the extent of topological differences between
two given trees. For unrooted, bifurcating trees, this distance is twice the number
of interior branches at which the taxa are partitioned differently.
7.460 Topology
The branching pattern of a tree is its topology.
206
Molecular Evolutionary Genetics Analysis
7.461 Transition
A transition occurs when a purine is substituted by a purine, or a pyrimidine by a
pyrimidine.
7.462 Transition Matrix
A transition matrix specifies the probability of every possible substitution among
the nucleotides or amino acids.
7.463 Transition/Transversion Ratio (R)
This is the ratio of the number of transitions to the number of transversions for a
pair of sequences. R becomes 0.5 when there is no bias towards either transitional
or transversional substitution because, when the two kinds of substitution are
equally probable, there are twice as many possible transversions as transitions.
MEGA allows you to conduct an analysis of your data with a specified value of R.
Note that R should not be confused with the ratio of the transition and transversion
rates (k = α/β).
7.464 Translation
Translation is the process whereby each codon in the mRNA is translated into a
particular amino acid, according to the genetic code specific to the species and its
DNA, and added to the growing polypeptide chain.
7.465 Transversion
A change from a purine to a pyrimidine, or vice versa, is a transversion.
7.466 Tree length
Tree length is the criterion used by the Maximum Parsimony method to search for
the best tree. It is defined as the sum of the minimum numbers of substitutions
over all sites for the given topology.
To compute the tree length for the unweighted parsimony method, we use the
procedure described in Fitch (1971), which is based on the two rules described
below. For a given site these rules are applied to each node and the sum of
substitutions over all nodes and over all sites is taken. Note that the estimation of
the minimum number of substitutions is not affected by the position of the root.
Rule 1. When the two descendent nodes of an ancestral node have some states
(nucleotides or amino acids) in common, the ancestral node is assigned to the set of
common states. In this case, the most parsimonious explanation does not require
any substitutions.
Rule 2. When the two descendant nodes have no states in common, then all states
in the descendent nodes are combined to form the set of possible states at the
ancestral node. In this case, one substitution is required.
207
Appendix
7.467 Unrooted tree
An unrooted tree is one in which no assumption is made regarding the ancestor of
all the taxa in the tree.
7.468 Variable site
A variable site contains at least two types of nucleotides or amino acids. Some
variable sites can be singleton or parsimony-informative. A site that is not variable
is referred to as a constant site.
7.5 Reference
Comeron JM (1995) A method for estimating the numbers of synonymous and nonsynonymous
substitutions per site. Journal of Molecular Evolution 41:1152-1159.
Dayhoff MO (1978) Survey of new data and computer methods of analysis. In Dayhoff MO, ed., Atlas
of Protein Sequence and Structure, vol. 5, supp. 3, pp. 29, National Biomedical Research Foundation,
Silver Springs, Maryland.
Schwarz R & Dayhoff M (1979) Matrices for detecting distant relationships.
In Dayhoff M, editor, Atlas of protein sequences, pages 353 - 58. National
Biomedical Research Foundation.
DeBry RW (1992) The consistency of several phylogeny-inference methods under varying
evolutionary rates. Molecular Biology and Evolution 9:537-551.
Dopazo J (1994) Estimating errors and confidence intervals for branch lengths in phylogenetic trees
by a bootstrap approach. Journal of Molecular Evolution 38:300-304.
Eck RV & Dayhoff MO (1966) Atlas of Protein Sequence and Structure. National Biomedical Research
Foundation, Silver Springs, Maryland.
Efron B (1982) The Jackknife, the Bootstrap and Other Resampling Plans. CBMS-NSF Regional
Conference Series in Applied Mathematics, Monograph 38, SIAM, Philadelphia.
Estabrook GF, Johnson CS & McMorris FR (1975) An idealized concept of the true cladistic character.
Mathematical Biosciences 23:263-272.
208
Molecular Evolutionary Genetics Analysis
Felsenstein J (1978) Cases in which parsimony or compatibility methods will be positively misleading.
Systematic Zoology 27:401-410.
Felsenstein J (1985) Confidence limits on phylogenies: An approach using the bootstrap. Evolution
39:783-791.
Felsenstein J (1986) Distance Methods: Reply to Farris. Cladistics 2:130-143.
Felsenstein J (1988) Phylogenies from molecular sequences: Inference and reliability. Annual Review
of Genetics 22:521-565.
Felsenstein J (1993) Phylogeny Inference Package (PHYLIP). Version 3.5. University of Washington,
Seattle.
Felsenstein J & Kishino H (1993) Is there something wrong with the bootstrap on phylogenies? A reply
to Hillis and Bull. Systematic Biology 42:193-200.
Fitch WM (1971) Towards defining the course of evolution: Minimum change for a specific tree
topology. Systematic Zoology 20:406-416.
Fitch WM & Margoliash E (1967) Construction of phylogenetic trees. Science 155:279 284.
Goldman N (1993) Statistical tests of models of DNA substitution. Journal of Molecular Evolution
36:182-198.
Gu X & Zhang J (1997) A simple method for estimating the parameter of substitution rate variation
among sites. Molecular Biology and Evolution 15:1106-1113.
Hedges SB, Kumar S, Tamura K, & Stoneking M (1992). Human origins and analysis of mitochondrial
DNA sequences. Science 255:737-739.
Hendy MD & Penny (D) (1982) Branch and bound algorithms to determine minimal evolutionary trees.
Mathematical Biosciences 59:277-290.
Hendy M D & Penny D (1989) A framework for the quantitative study of evolutionary trees. Systematic
Zoology 38:297-309.
209
Appendix
Hillis DM & Bull JJ (1993) An empirical test of bootstrapping as a method for assessing confidence in
phylogenetic analysis. Systematic Biology 42:182-192.
Hillis DM, Moritz C & Mable BK (1996) Molecular Systematics. 2 edition.
Sunderland, MA: Sinauer Associates, Inc.
Jones DT, Taylor WR & Thornton JM (1992) The rapid generation of mutation
data matrices from protein sequences. Computer Applications in the Biosciences 8:
275-82.
Jukes TH & Cantor CR (1969) Evolution of protein molecules. In Munro HN, editor, Mammalian
Protein Metabolism, pp. 21-132, Academic Press, New York.
Kimura M (1980) A simple method for estimating evolutionary rate of base substitutions through
comparative studies of nucleotide sequences. Journal of Molecular Evolution 16:111-120.
Kishino H & Hasegawa M (1989) Evaluation of the maximum likelihood estimate of the evolutionary
tree topologies from DNA sequence data, and the branching order in Hominoidea. Journal of
Molecular Evolution 29:170- 179.
Kumar, S. and S. R. Gadagkar (2001) Disparity Index: A simple statistic to measure and test the
homogeneity of substitution patterns between molecular sequences. Genetics 158:1321-1327.
Kumar S, Tamura K & Nei M (1993) MEGA: Molecular Evolutionary Genetics Analysis. Pennsylvania
State University, University Park, PA.
Kumar S, Tamura K & Nei M (2004) MEGA3: Integrated Software for Molecular Evolutionary Genetics
Analysis and Sequence Alignment. Briefings in Bioinformatics 5:150-163.
Lake JA (1987) A rate-independent technique for analysis of nucleic acid sequences: Evolutionary
parsimony. Molecular Biology and Evolution 4:167-191.
Li W-H (1993) Unbiased estimation of the rates of synonymous and nonsynonymous substitution.
Journal of Molecular Evolution 36:96-99.
Li W-H (1997) Molecular Evolution. Sunderland, MA: Sinauer Associates.
210
Molecular Evolutionary Genetics Analysis
Li W-H, Wu C-I & Luo C-C (1985) A new method for estimating synonymous and nonsynonymous
rates of nucleotide substitution considering the relative likelihood of nucleotide and codon changes.
Molecular Biology and Evolution 2:150-174.
Maddison WP & Maddison DR (1992) MacClade: Analysis of phylogeny and character evolution.
Version 3. Sinauer Associates, Sunderland, Massachusetts.
Nei M (1986) Stochastic errors in DNA evolution and molecular phylogeny. In Gershowitz H,
Rucknagel DL, & Tashian RE, editors, Evolutionary Perspectives and the New Genetics. pp. 133-147.
Alan R. Liss, New York.
Nei M (1991) Relative efficiencies of different tree making methods for molecular data. In Miyamoto
MM and Cracraft JL, editors, Recent Advances in Phylogenetic Studies of DNA Sequences, pp. 90128. Oxford University Press, Oxford.
Nei M & Gojobori T (1986) Simple methods for estimating the numbers of synonymous and
nonsynonymous nucleotide substitutions. Molecular Biology and Evolution 3:418-426.
Nei M & Jin L (1989) Variances of the average numbers of nucleotide substitutions within and
between populations. Molecular Biology and Evolution 6:290-300.
Nei M & Kumar S (2000) Molecular Evolution and Phylogenetics. Oxford University Press, New York.
Nei M, Chakraborty R & Fuerst PA (1976) Infinite allele model with varying mutation rate. Proceedings
of National Academy of Sciences (USA) 73:4164-4168.
Nei M, Stephens JC & Saitou N (1985) Methods for computing the standard errors of branching points
in an evolutionary tree and their application to molecular data from humans and apes. Molecular
Biology and Evolution 2:66-85.
Nei M, Kumar S & Takahashi (1998) The optimization principle in phylogenetic analysis tends to give
incorrect topologies when the number of nucleotides or amino acids used is small. Proceedings of
National Academy of Sciences (USA) 95:12390-12397
Page RDM & Holmes EC (1998) Molecular Evolution: A Phylogenetic Approach. Blackwell Science,
Oxford, U.K.
Pamilo P& Bianchi NO (1993) Evolution of the Zfx and Zfy, genes: Rates and interdependence
211
Appendix
between the genes. Molecular Biology and Evolution 10:271-281.
Penny D & Hendy MD (1985) The use of tree comparison metrics. Systematic Zoology 34:75-82.
Pamilo P & Nei M (1988) Relationships between gene trees and species trees. Molecular Biology and
Evolution 5:568-583.
Press WH, Flannery BP, Teukolsky SA & Vetterling WT (1989) Numerical Recipes in Pascal: The Art
of Scientific Computing. Cambridge University Press, New York.
Purdom PW, Bradford PG, Tamura K & Kumar S (2000) Single column
discrepancy and dynamic max-mini optimizations for quickly finding the most
parsimonious evolutionary trees. Bioinformatics 16:140-151.
Rzhetsky A & Nei M (1992) A simple method for estimating and testing minimum evolution trees.
Molecular Biology and Evolution 9:945-967.
Rzhetsky A & Nei M (1993) Theoretical foundation of the minimum-evolution method of phylogenetic
inference. Molecular Biology and Evolution 10:1073-1095.
Saitou N & Nei M (1987) The neighbor-joining method: A new method for reconstructing phylogenetic
trees. Molecular Biology and Evolution 4:406-425.
Sankoff D & Cedergren RJ (1983) Simultaneous comparison of three or more sequences related by a
tree. In Sankoff D & Kruskal JB, editors., Time Warps, String Edits, and Macromolecules: The Theory
and Practice of Sequence Comparison, pp. 253-263. Addison-Wesley, Reading, Massachusetts.
Sharp PM, Tuohy TMF & Mosurski KR (1986) Codon usage in yeast: Cluster analysis clearly
differentiates highly and lowly expressed genes. Nucleic Acids Research 14:5125-5143.
Sneath PHA & Sokal RR (1973) Numerical Taxonomy. Freeman, San Francisco.
Sourdis J & Krimbas C (1987) Accuracy of phylogenetic trees estimated from DNA sequence data.
Molecular Biology and Evolution 4:159-166.
Sourdis J & Nei M (1988) Relative efficiencies of the maximum parsimony and distance-matrix
methods in obtaining the correct phylogenetic tree. Molecular Biology and Evolution 5:298-311.
212
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Studier, J. A. and K. L. Keppler. 1988. A note on the neighbor-joining algorithm of Saitou and Nei.
Molecular Biology and Evolution 5:729-731.
Swofford DL (1993) Phylogenetic Analysis Using Parsimony (PAUP), Version 3.1.1. University of
Illinois, Champaign.
Swofford DL (1998) PAUP*: Phylogenetic Analysis Using Parsimony (and Other Methods)
Sunderland, MA: Sinauer Associates.
Swofford DL, Olsen GJ, Waddell PJ & Hillis DM (1996). Phylogenetic Inference. In Hiillis DM, Moritz
D, and Mable BK, editors, Molecular Systematics, pp. 407-514.Sinauer Associates, Sunderland,
Massachusetts.
Tajima F (1983) Evolutionary relationship of DNA sequences in finite populations. Genetics 105:437460.
Tajima F (1989) Statistical methods to test for nucleotide mutation hypothesis by DNA polymorphism.
Genetics 123:585-595.
Tajima F (1993) Simple methods for testing molecular clock hypothesis. Genetics 135:599-607.
Tajima F & Nei M (1982) Biases of the estimates of DNA divergence obtained by the restriction
enzyme technique. Journal of Molecular Evolution 18:823-833.
Tajima F & Nei M (1984) Estimation of evolutionary distance between nucleotide sequences.
Molecular Biology and Evolution 1:269-285.
Takahashi K & Nei M (2000) Efficiencies of fast algorithms of phylogenetic inference under the criteria
of maximum parsimony, minimum evolution, and maximum likelihood when a large number of
sequences are used. Molecular Biology and Evolution 17:1251-1258.
Takezaki N, Rzhetsky A & Nei M (2004) Phylogenetic test of the molecular clock and linearized trees.
Molecular Biology and Evolution 12:823-833.
Tamura K (1992) Estimation of the number of nucleotide substitutions when there are strong
transition-transversion and G + C-content biases. Molecular Biology and Evolution 9:678-687.
213
Appendix
Tamura K (1994) Model selection in the estimation of the number of nucleotide substitutions.
Molecular Biology and Evolution 11:154-157.
Tamura K and S Kumar (2002) Evolutionary distance estimation under
heterogeneous substitution pattern among lineages Molecular Biology and
Evolution 19:1727-1736.
Tamura K & Nei M (1993) Estimation of the number of nucleotide
substitutions in the control region of mitochondrial DNA in humans and
chimpanzees. Molecular Biology and Evolution 10:512-526.
Tamura K, Nei M & Kumar S (2004) Prospects for inferring very large phylogenies by using the
neighbor-joining method. Proceedings of the National Academy of Sciences (USA) 101:11030-11035.
Tanaka T & Nei M (1989) Positive Darwinian selection observed at the variable-region genes of
immunoglobulins. Molecular Biology and Evolution 6:447-459.
Tateno Y, Nei M & Tajima F (1982) Accuracy of estimated phylogenetic trees from molecular data. I.
Distantly related species. Journal of Molecular Evolution 18:387-404.
Tateno Y, Takezaki N & Nei M (1994) Relative efficiencies of the maximum likelihood, neighborjoining, and maximum parsimony methods when substitution rate varies with site. Molecular Biology
and Evolution 11:261-277.
Yang Z (1999) PAML: Phylogenetic analysis by maximum likelihood, Version 2.0. University College
London, London.
Zhang J & Gu X (1998) Correlation between the substitution rate and rate variation among sites in
protein evolution. Genetics 149:1615-1625.
Zhang J, Kumar S & Nei M (1997) Small-sample tests of episodic adaptive evolution: a case study of
primate lysozymes. Molecular Biology and Evolution 14:1335-1338.
Zhang J, Rosenberg HF & Nei M (1998). Positive Darwinian selection after
gene duplication in primate ribonuclease genes. Proceedings of the National
Academy of Sciences (USA) 95:3708-3713.
Zuckerkandl E & Pauling L (1965) Evolutionary divergence and convergence in proteins, pp. 97-166 in
Evolving Genes and Proteins, edited by V. Bryson and H.J. Vogel. Academic Press, New York.
214
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Index
0
0-fold site.............................................................................................................................................. 153
2
2-Dimensional Data Grid ....................................................................................................... 97, 107, 174
2-fold site.............................................................................................................................................. 152
2S-fold site ........................................................................................................................................... 152
2V-fold site........................................................................................................................................... 153
4
4-fold site...................................................................................................................... 148, 150, 152, 184
A
ABI File Format ................................................................................................................................... 205
About BLAST ........................................................................................................................................55
About CLUSTALW ...............................................................................................................................52
About dialog ......................................................................................................................................... 196
Acknowledgements ................................................................................................................................16
Add button............................................................................................................................................ 197
Add taxa ....................................................................................................................................... 197, 198
Add/Insert............................................................................................................................................. 198
Add/Remove Programs ..........................................................................................................................19
Adding/Modifying Genetic Code Tables................................................................................................93
Alanine ...................................................................................................................................................65
Aligning coding sequences via protein sequences..................................................................................51
Alignment Builder ..................................................................................................................................51
Alignment Explorer/CLUSTAL ........................................................................................................... 195
Alignment Gap ..................................................................................................................... 161, 165, 196
Alignment Menu................................................................................................................................... 195
Alignment Menu in Alignment Explorer................................................................................................57
Alignment session ................................................................................................................................ 205
215
Amino Acid Compositions ........................................................................................................... 105, 114
Analysis Preferences .................................................................................... 154, 157, 159, 161, 168, 169
Analysis Preferences dialog ................................................................................................................. 113
Analysis Preferences/Options dialog.................................................................................................... 113
Arrange Taxa........................................................................................................................................ 180
ASCII ......................................................................................................................... 45, 62, 64, 110, 111
editing.................................................................................................................................................45
ASCII-text ..............................................................................................................................................62
Asparagine..............................................................................................................................................66
Aspartic Acid..........................................................................................................................................65
Assigning.............................................................................................................................................. 200
exons................................................................................................................................................. 200
Average Menu .............................................................................................................................. 108, 175
B
Basic Sequence Statistics ..................................................................................................................... 114
BCL ...................................................................................................................................................... 164
Between Groups ................................................................................................................................... 175
Bidirectionally ...................................................................................................................................... 105
Bifurcating Tree ................................................................................................................................... 205
Blank Names Are Not Permitted .......................................................................................................... 201
BLAST Search.........................................................................................................................................55
Bootstrap method ................................................................................................................................. 156
compute standard error ..................................................................................................................... 156
Bootstrap Test....................................................................................................................................... 165
Bootstrap Test of Phylogeny................................................................................................................. 165
Branch Length ...................................................................................................................................... 181
Branch Line .......................................................................................................................................... 181
Branch tab ............................................................................................................................................ 181
Branch-and-bound ........................................................................................................................ 161, 192
Browse Databanks................................................................................................................................ 195
Bugs........................................................................................................................................................30
Reporting ............................................................................................................................................30
216
Molecular Evolutionary Genetics Analysis
Built-in Genetic Codes ...........................................................................................................................91
C
Caption Expert...................................................................................................................................... 182
Captions................................................................................................................................................ 182
Categorize............................................................................................................................................. 107
taxa ................................................................................................................................................... 107
Change Font ................................................................................................................................. 107, 175
Change Font dialog box........................................................................................................................ 102
Change Font.Display ............................................................................................................................ 102
Choose Model....................................................................................................................................... 188
Circle .................................................................................................................................................... 180
Citing MEGA in Publications.................................................................................................................17
Classroom...............................................................................................................................................30
Clipboard.............................................................................................................................................. 111
Close Data............................................................................................................................................ 186
Close-Neighbor-Interchange ................................................................................................................ 162
CLUSTAL ..............................................................................................................................................73
ClustalW............................................................................................................................................... 206
CLUSTALW Options DNA ...................................................................................................................52
CLUSTALW Options Protein ................................................................................................................53
CNI 163, 193
Code Table .............................................................................................................................................93
Code Table Editor ..................................................................................................................................95
Coding .................................................................................................................... 66, 67, 68, 96, 97, 200
DNA ...................................................................................................................................................96
Codon91, 93, 94, 97, 99, 105, 114, 148, 154, 155, 157, 158, 159, 160, 162, 169, 170, 184, 188, 200, 201, 206
find .....................................................................................................................................................95
inclusion/exclusion ................................................................................... 154, 157, 159, 161, 168, 169
position ...............................................................................................................................................94
Codon based Z-test ............................................................................................................................... 183
Codon Usage ................................................................................................................................ 105, 206
Color Cells............................................................................................................................................ 100
217
Column Sizer................................................................................................................................ 107, 175
Command Statements................................................................................................................. 67, 68, 69
Keywords............................................................................................................................................68
Writing ......................................................................................................................................... 67, 69
Common Features...................................................................................................................................62
Common Sites ...................................................................................................................................... 203
Complete-Deletion ............................................................................................................................... 165
Complex 2-fold sites............................................................................................................................. 152
Composition Distance .......................................................................................................................... 206
Compute Between Groups Means......................................................................................................... 190
Compute Composition Distance........................................................................................................... 173
Compute Menu ..................................................................................................................................... 182
Compute Net Between Groups Means .................................................................................................. 190
Compute Overall Mean ........................................................................................................................ 189
Compute Pairwise................................................................................................................................. 188
Compute Pattern Disparity Index ......................................................................................................... 172
Compute Sequence Diversity ................................................................................................................ 189
Compute standard error ........................................................................................................................ 156
Bootstrap method ............................................................................................................................. 156
Compute Substitution Pattern............................................................................................................... 172
Compute Transition/Transversion Bias ................................................................................................ 173
Compute Within Groups Mean............................................................................................................. 189
Computing ...................................................................................................................... 93, 144, 152, 183
Statistical Attributes ...........................................................................................................................93
statistics ............................................................................................................................................ 183
Computing Statistical Quantities for Nucleotide Sequences ..................................................................40
Computing the Gamma Parameter (a) .................................................................................................. 125
Condensed Trees .................................................................................................................................. 164
Construct ...................................................................................................................... 157, 164, 192, 193
Construct Phylogeny ............................................................................................................................ 191
Constructing Trees and Selecting OTUs from Nucleotide Sequences ...................................................35
Constructing Trees from Distance Data .................................................................................................42
218
Molecular Evolutionary Genetics Analysis
Convert To MEGA Format Main File Menu.......................................................................................... 75
Copy ..................................................................................................................................................... 111
Copyright................................................................................................................................................16
CPU ........................................................................................................................................................19
Create New Folder ............................................................................................................................... 186
Creating Data Captions with Caption Expert ....................................................................................... 182
Creating Multiple Sequence Alignments................................................................................................32
Curved .................................................................................................................................................. 180
Cut 111
Cutoff Values Tab ................................................................................................................................ 180
Cysteine ..................................................................................................................................................65
D
Data .............................................................................................................................. 196, 197, 199, 203
Missing ..................................................................................................................................... 196, 203
Data | Data Explorer ..................................................................................................................... 186, 187
Data | Quit Data Viewer .........................................................................................................................99
Data | Select Genetic Code Table..................................................................................................... 94, 98
Data | Select Preferences .............................................................................................................. 187, 188
Data | Setup/Select Genes............................................................................................................... 98, 112
Data | Setup/Select Taxa................................................................................................................... 69, 98
Data | Translate/Untranslate ...................................................................................................................98
Data | Write Data ....................................................................................................................................99
Data Description Window ............................................................................................................ 184, 185
Data Explorer ................................................................................................. 97, 183, 186, 187, 197, 203
Data File Parsing Error......................................................................................................................... 201
Data menu .................................................................................................................... 31, 94, 97, 98, 186
Data Menu in Alignment Explorer .........................................................................................................59
Data Type ............................................................................................................................. 187, 196, 197
Datafile ...................................................................................................................................................63
DataFormat.............................................................................................................................................72
Dataset.......................................................................................... 72, 96, 97, 98, 105, 114, 186, 202, 203
DataType .................................................................................................................................... 64, 66, 72
219
Dayhoff 1979........................................................................................................................................ 218
Dayhoff and JTT distances Gamma rates ............................................................................................. 144
Dayhoff distance........................................................................................................................... 144, 218
Dayhoff Distance Could Not Be Computed ......................................................................................... 201
Dayhoff Model ..................................................................................................................................... 143
Define/Edit/Select ........................................................................................................................ 198, 200
Defining Genes.......................................................................................................................................67
Defining Groups .....................................................................................................................................69
DefiningTaxa..........................................................................................................................................69
Description Statement Rules ..................................................................................................................63
Disclaimer ..............................................................................................................................................16
Discrete-character................................................................................................................................. 157
Disparity Index ..................................................................................................................................... 207
Display ................................................................................................................................................. 193
UPGMA ........................................................................................................................................... 193
Display | Color...................................................................................................................................... 100
Display | Restore Input Order ............................................................................................................... 100
Display | Show...................................................................................................................................... 100
Display | Show Group Names .............................................................................................................. 102
Display | Show Sequence Names ......................................................................................................... 102
Display | Sort Sequences .............................................................................................................. 102, 103
Display | Use Identical Symbol ............................................................................................................ 101
Display font .......................................................................................................................................... 102
Display Menu ......................................................................................................................... 99, 107, 175
Display Menu in Alignment Explorer ....................................................................................................57
Display Newick Trees from File ............................................................................................................73
Display Saved Tree Session ................................................................................................................. 191
Distance Computation .......................................................................................................................... 154
Distance Correction Failed ................................................................................................................... 203
Distance Data Explorer................................................................................. 106, 107, 108, 113, 174, 176
Distance Data Formats ...........................................................................................................................71
Distance Data Subset Selection ............................................................................................................ 113
220
Molecular Evolutionary Genetics Analysis
Distance Display Precision........................................................................................................... 106, 174
Distance estimates ................................................................................................................................ 156
Distance Matrix Dialog ........................................................................................................ 202, 203, 204
Distance Matrix Explorer ............................................................................................. 108, 174, 175, 176
Distance menu .............................................................................................................. 183, 184, 188, 189
Distance Model Options ....................................................................................................................... 156
Distance Options .......................................................................................................................... 168, 169
Distances .................................................................................................................. 95, 98, 114, 116, 183
Distances | Choose Model .................................................................................................................... 188
Distances | Compute Between Groups Means.Choose ......................................................................... 190
Distances | Compute Net Between Groups Means.Choose .................................................................. 190
Distances | Compute Overall Mean ...................................................................................................... 189
Distances | Compute Pairwise .............................................................................................................. 188
Distances | Compute Sequence Diversity ............................................................................................. 189
Distances | Compute Within Groups Means.Choose............................................................................ 189
Distances Display Box ......................................................................................................................... 108
Distances Menu .................................................................................................................................... 188
Divergence Time .................................................................................................................. 178, 179, 182
Divergence Time Dialog Box............................................................................................................... 180
DNA ....................................................................................... 62, 66, 73, 95, 99, 100, 114, 157, 165, 187
coding .................................................................................................................................................95
reading data from other formats .........................................................................................................73
DNA/RNA ....................................................................................................................................... 65, 202
Do BLAST Search..................................................................................................................................55
Domain Editor ...................................................................................................................................... 198
Domains ........................................................................................................................... 67, 98, 105, 112
Domains Cannot Overlap ..................................................................................................................... 201
Domains Dialog.................................................................................................................................... 198
Drag-and-drop ...................................................................................................................................... 199
Drosophila mitochondrial genetic code table .........................................................................................91
E
Edit | Copy............................................................................................................................................ 111
221
Edit | Cut............................................................................................................................................... 111
Edit | Font ............................................................................................................................................. 111
Edit | Paste ............................................................................................................................................ 111
Edit | Undo............................................................................................................................................ 111
Edit menu ...............................................................................................................................................46
Edit Menu in Alignment Explorer ..........................................................................................................58
Edit Sequencer Files ............................................................................................................................. 195
Edits........................................................................................................................................................45
ASCII .................................................................................................................................................45
EMF...................................................................................................................................................... 178
End 66, 68
Entire Population.................................................................................................................................. 189
Mean Diversity ................................................................................................................................. 189
Equal Input Correction Failed .............................................................................................................. 201
Equal Input Model................................................................................................................................ 142
Equal Input Model Gamma .................................................................................................................. 125
Equal Input Model Gamma rates and Heterogeneous Patterns............................................................. 136
Equal Input Model Heterogeneous Patterns ......................................................................................... 145
Estimate ........................................................................................................................................ 144, 189
Dayhoff distance............................................................................................................................... 144
interpopulational diversity................................................................................................................ 189
Estimating Evolutionary Distances from Nucleotide Sequences............................................................33
Exclude/include sites ..............................................................................................................................99
Exit 110, 176, 186, 187
Distance Data Explorer..................................................................................................................... 176
MEGA .............................................................................................................................................. 187
Exit Tree Explorer ................................................................................................................................ 177
Exon ......................................................................................................................................... 66, 68, 200
Expand/contract box............................................................................................................................. 199
Export All Trees ................................................................................................................................... 177
Export Current Tree.............................................................................................................................. 177
Export Data..................................................................................................................................... 99, 186
222
Molecular Evolutionary Genetics Analysis
Export/Print Distances.................................................................................................................. 107, 176
Exporting Sequence Data .......................................................................................................................99
Exporting Sequence Data dialog ............................................................................................................97
F
Feature List.............................................................................................................................................20
Figure Legend....................................................................................................................................... 182
File menu.................................................................................................................. 46, 47, 107, 176, 184
File Menu ............................................................................................................................................. 187
File Name ............................................................................................................................................. 185
Files ........................................................................................................................................ 99, 177, 186
Data | Write Data ................................................................................................................................99
Tree Explorer.................................................................................................................................... 177
Type.......................................................................................................................................... 185, 186
Files Of Type ........................................................................................................................................ 186
Find................................................................................................................. 94, 112, 163, 183, 192, 193
codon ..................................................................................................................................................94
ME.................................................................................................................................................... 192
MP ............................................................................................................................................ 163, 193
number.............................................................................................................................................. 183
Find Again............................................................................................................................................ 112
Find Text dialog ................................................................................................................................... 112
Fisher's Exact Test........................................................................................................................ 169, 194
Selection ........................................................................................................................................... 194
Fisher's Exact Test Has Failed.............................................................................................................. 202
Fixed Column ......................................................................................................................... 95, 106, 174
Fixed Row .............................................................................................................................. 95, 106, 174
Font....................................................................................................................................................... 111
Font dialog...................................................................................................................................... 99, 181
Format dialog ....................................................................................................................................... 196
Format Statement ....................................................................................................................... 64, 66, 72
Keywords...................................................................................................................................... 66, 72
Rules...................................................................................................................................................64
223
Formats..................................................................................................................................... 62, 71, 185
G
G+C-content ................................................................................................................................. 121, 204
Gamma ................................................................................. 126, 127, 128, 129, 130, 131, 144, 145, 202
Gamma Correction Failed Because p ................................................................................................... 202
Gamma distance ................................................................................................................................... 144
Gamma model ...................................................................................................................................... 129
Gaps ..................................................................................................................................................... 188
Handling........................................................................................................................................... 188
Gene Names Must Be Unique .............................................................................................................. 202
General Comments on Statistical Tests ................................................................................................ 163
General Considerations .................................................................................................................... 64, 71
Genes...................................................................................................................................... 67, 198, 199
Genes/Domains ......................................................................................................................................68
Genes\Domain ...................................................................................................................................... 198
Genetic Code ..........................................................................................................................................93
Glutamic Acid ........................................................................................................................................65
Glycine ...................................................................................................................................................65
Gojobori ............................................................................................................................................... 170
Grid......................................................................................................................................... 97, 107, 175
Grishin's distance.................................................................................................................................. 144
Group Name ......................................................................................................................................... 102
Groups ............................................................................ 69, 70, 95, 97, 98, 102, 106, 108, 175, 189, 190
taxa ................................................................................................. 69, 95, 97, 106, 108, 175, 189, 190
Groups Dialog ...................................................................................................................................... 197
Gu and Zhang 1997 .............................................................................................................................. 219
Gu and Zhang 1998 .............................................................................................................................. 219
H
Hand-with-a-pencil icon....................................................................................................................... 197
Help ........................................................................................................................................ 17, 185, 198
Help | About.......................................................................................................................................... 196
Help Index ............................................................................................................................................ 196
224
Molecular Evolutionary Genetics Analysis
Help menu .............................................................................................................................. 17, 184, 196
Hiding taxa ........................................................................................................................................... 175
Highlight | Parsim-Info Sites ................................................................................................................ 104
Highlight 0-fold Degenerate Sites ........................................................................................................ 104
Highlight 2-fold Degenerate Sites ........................................................................................................ 104
Highlight 4-fold Degenerate Sites ........................................................................................................ 104
Highlight Conserved Sites .................................................................................................................... 103
Highlight Menu .................................................................................................................................... 103
Highlight Singleton Sites...................................................................................................................... 103
Highlight Variable Sites ....................................................................................................................... 103
Highlighted Sites .................................................................................................................................. 106
Highlighting............................................................................................................................................96
Sites ....................................................................................................................................................95
Hillis et al. 1996 ................................................................................................................................... 219
Histidine .................................................................................................................................................65
I
ID 102, 103
Identical..................................................................................................................................................67
Identical Symbol................................................................................................................................... 196
Image Menu.......................................................................................................................................... 178
Importing Data From Other Formats......................................................................................................73
Inapplicable Computation Requested ................................................................................................... 202
Include Codon Positions....................................................................................................................... 187
Include Labeled Sites ........................................................................................................................... 187
Include Sites Option ............................................................................................................................. 166
Include/exclude ......................................................................................................................................95
Include/Exclude taxa ............................................................................................................ 106, 174, 198
Including................................................................................................................................... 18, 73, 180
CLUSTAL ..........................................................................................................................................73
MEGA .......................................................................................................................................... 17, 18
taxon ................................................................................................................................................. 180
Inclusion/exclusion of codon positions/labeled sites.................................... 154, 157, 159, 161, 168, 169
225
Inconsistencies................................................................................................................................ 30, 185
Incorrect Command Used..................................................................................................................... 202
Increase/decrease.......................................................................................................................... 106, 174
Indel................................................................................................................................................ 67, 161
Independents node................................................................................................................................ 198
Index ..................................................................................................................................................... 196
Information Box ................................................................................................................................... 177
Input Data Format Dialog..................................................................................................................... 196
Insert genes or domains ........................................................................................................................ 198
Insertions/deletions............................................................................................................................... 161
Installing MEGA ....................................................................................................................................19
Intergenic domains ............................................................................................................................... 199
Interior Branch Test ............................................................................................................................. 164
Interpopulational diversity.................................................................................................................... 190
estimate..................................................................................................................................... 189, 190
Introduction to Walk Through MEGA ...................................................................................................31
Intron ........................................................................................................................................ 66, 68, 200
Intron Property ................................................................................................................................. 67, 69
Invalid distances ................................................................................................................................... 203
Invalid special symbol.......................................................................................................................... 202
Isoleucine ...............................................................................................................................................66
IUPAC single letter codes ......................................................................................................................64
J
Jones et al. 1992 ................................................................................................................................... 220
Jukes-Cantor......................................................................................................... 119, 146, 147, 202, 203
Jukes-Cantor Correction Failed ............................................................................................................ 202
Jukes-Cantor distance........................................................................................................................... 118
Jukes-Cantor Gamma distance ............................................................................................................. 126
K
Keywords ................................................................................................................................... 66, 68, 72
Command Statements.........................................................................................................................68
Format Statement ......................................................................................................................... 66, 72
226
Molecular Evolutionary Genetics Analysis
Kimura 2-parameter distance................................................................................................................ 120
Kimura gamma distance ....................................................................................................................... 127
Kimura-2-parameter-Gamma distance ................................................................................................. 127
Kumar et al. 2004 ................................................................................................................................. 220
Kumar Method ..................................................................................................................................... 152
[email protected] ............................................................................................................... 16, 30
L
Labels Tab ............................................................................................................................................ 200
Large Sample Tests of Selection .......................................................................................................... 167
Leaf taxa ............................................................................................................................................... 177
Leucine ...................................................................................................................................................66
Level of CP........................................................................................................................................... 164
Li 1993 ................................................................................................................................................. 220
Li 1997 ................................................................................................................................................. 220
Linux ......................................................................................................................................................19
Listing................................................................................................................................................... 197
taxa ........................................................................................................................................... 197, 198
Li-Wu-Luo............................................................................................................................................ 153
Li-Wu-Luo Method .............................................................................................................................. 148
LogDet Distance Could Not Be Computed .......................................................................................... 203
Look In.................................................................................................................................................. 186
M
Main MEGA Window .......................................................................................................................... 184
Managing Taxa With Groups .................................................................................................................40
Manipulating tree aspects ..................................................................................................................... 181
Marker Graphics................................................................................................................................... 181
MatchChar ..............................................................................................................................................67
Matrix ................................................................................................................................................... 203
Matrix Explorer .................................................................................................................................... 176
Matrix Format....................................................................................................................................... 197
Maximum Composite Likelihood................................................................................................. 124, 210
Maximum Composite Likelihood Gamma Rates and Heterogeneous Patterns .................................... 140
227
Maximum Composite Likelihood Heterogeneous Patterns .................................................................. 136
Maximum Composite Likelihood Method ................................................................................... 124, 163
Maximum Composite_Likelihood Gamma .......................................................................................... 132
Maximum Parsimony ........................................................................................................... 161, 192, 193
Maximum-likelihood.................................................................................................................... 164, 192
Max-mini branch-and-bound search..................................................................................................... 210
ME 158, 159, 164, 191, 192
ME Tree Tab ........................................................................................................................................ 192
Mean Diversity ..................................................................................................................................... 189
Entire Population .............................................................................................................................. 189
Interpopulational Diversity............................................................................................................... 189
MEG ..................................................................................................................................................... 185
MEGA
citing...................................................................................................................................................17
classroom use .....................................................................................................................................30
exiting............................................................................................................................................... 187
Installing.............................................................................................................................................19
MEGA Format........................................................................................................................................62
MEGA Software Development Team ....................................................................................................17
Menu bar ................................................................................................................................................45
Menus ................................................................................................................................................... 183
Methionine..............................................................................................................................................66
Microsoft Word ......................................................................................................................................62
Midpoint ............................................................................................................................................... 180
Minimum Evolution ..................................................................................................................... 159, 192
Minimum Evolution Construct Phylogeny........................................................................................... 192
Missing ................................................................................................................................. 196, 197, 203
data ................................................................................................................................................... 203
Data .................................................................................................................................................. 196
Missing Data ........................................................................................................................................ 188
Missing Information ............................................................................................................. 161, 165, 166
Model button ........................................................................................................................................ 228
228
Molecular Evolutionary Genetics Analysis
Models.......................................................................................................................................... 114, 119
Nei ............................................................................................................................................ 119, 120
Modified Nei-Gojobori......................................................................................................................... 169
Modified Nei-Gojobori Method ........................................................................................................... 147
Molecular sequences ............................................................................................................................ 202
Monophyletic........................................................................................................................................ 210
MP 161, 162, 164, 192
constructing ...................................................................................................................................... 192
find ........................................................................................................................................... 162, 192
produce ............................................................................................................................................. 193
MP Tree Tab......................................................................................................................................... 192
Options dialog .................................................................................................................................. 192
MP Trees .............................................................................................................................................. 192
Multifurcating tree................................................................................................................................ 164
N
Name ...................................................................................................... 95, 107, 174, 175, 176, 185, 186
sequences/groups...................................................................................................................... 106, 174
taxa ................................................................................................................................................... 175
NCBI ............................................................................................................................................ 210, 211
Nei et al. 1998 ...................................................................................................................................... 221
Neighbor Joining .................................................................................................................................. 164
Neighbor Joining Construct Phylogeny................................................................................................ 164
Neighbor-Joining.................................................................................................................................. 191
Nei-Gojobori .................................................................................................................................. 93, 147
Nei-Gojobori Method ........................................................................................................................... 146
Net Between Groups..................................................................................................................... 108, 175
Neutrality.............................................................................................................................................. 171
Tajima's Test..................................................................................................................................... 171
Tests | Tajima's Test ......................................................................................................................... 171
New ...................................................................................................................................................... 109
Newick Format ..................................................................................................................................... 211
Nex 73
229
Nexus/PAUP ..........................................................................................................................................73
NJ 159, 164, 165, 191, 192
NJ/UPGMA .......................................................................................................................................... 157
Noncoding ...................................................................................................................... 66, 67, 68, 69, 99
Nonsynonymous......................................................93, 146, 147, 148, 150, 152, 167, 168, 169, 194, 202
Nonsynonymous site .................................................................................... 148, 150, 152, 167, 194, 195
Notations Used .......................................................................................................................................31
Notepad ..................................................................................................................................................45
NSeqs ............................................................................................................................................... 66, 72
NSites .....................................................................................................................................................66
NT 19
NTaxa ............................................................................................................................................... 66, 72
Nucleotide ............................................................................................................................................ 114
Nucleotide Composition....................................................................................................................... 105
Nucleotide Pair Frequencies......................................................................................................... 105, 212
Nucleotide-by-nucleotide ..................................................................................................................... 114
Nucleotide-by-nucleotide site............................................................................................................... 187
Number93, 120, 122, 123, 127, 129, 130, 146, 147, 148, 149, 150, 151, 152, 159, 164, 167, 169, 174, 175, 183, 191, 194, 202
0-fold ........................................................................................................ 148, 149, 150, 151, 152, 153
4-fold ........................................................................................................ 148, 149, 150, 151, 152, 153
codons............................................................................................................................................... 169
Finding ............................................................................................................................................. 183
nonsynonymous...........................................93, 146, 147, 148, 149, 150, 151, 152, 153, 167, 194, 202
Sites .......................................................................................................................................... 146, 147
taxa ........................................................................................................................... 158, 164, 174, 191
transversional.................................................................................................... 120, 121, 123, 127, 129
O
OLS branch length estimates................................................................................................................ 212
Only 4-fold degenerate sites ................................................................................................................. 184
Writing ............................................................................................................................................. 184
Only highlighted sites........................................................................................................................... 183
Only Nei-Gojobori................................................................................................................................ 169
230
Molecular Evolutionary Genetics Analysis
Open ..................................................................................................................................................... 109
Open Data ............................................................................................................................................ 185
Open Saved Alignment Session .............................................................................................................49
Operational Taxonomic Units ................................................................................................................63
Options dialog ...................................................................................................... 108, 180, 181, 188, 192
MP Tree Tab..................................................................................................................................... 192
quit.................................................................................................................................................... 108
Order............................................................................................................................................. 180, 197
taxa ........................................................................................................................................... 180, 197
OTUs ...................................................................................................................................... 63, 157, 193
Outgroup............................................................................................................................................... 172
Outgroup taxa ....................................................................................................................................... 191
Output file ............................................................................................................................................ 184
P
Page and Holmes 1998 ......................................................................................................................... 221
Pairwise comparisons ........................................................................................................................... 183
Pairwise Deletion ................................................................................................................................. 166
Pairwise Distance Data................................................................................................................. 196, 197
Pairwise menu ...................................................................................................................................... 183
Pairwise-Deletion ................................................................................................................................. 165
Pamilo-Bianchi-Li ........................................................................................................................ 150, 153
Pamilo-Bianchi-Li Method................................................................................................................... 150
Parsimony-informative ......................................................................................................................... 192
Paste ..................................................................................................................................................... 111
Pattern Menu........................................................................................................................................ 172
PAUP 3.0................................................................................................................................................99
PAUP 4.0................................................................................................................................................99
P-distance ............................................................................................................................. 117, 141, 204
Phenylalanine .........................................................................................................................................65
Phy 73
PHYLIP..................................................................................................................................................73
PHYLIP 3.0 ............................................................................................................................................99
231
Phylogenetic ...... 18, 62, 114, 157, 159, 161, 164, 165, 187, 188, 190, 192, 193, 210, 219, 222, 223, 224
construct ................................................................................................................................... 157, 192
Phylogenetic Inference ......................................................................................................................... 157
Phylogenies .............................................................................................................................. 95, 98, 157
Phylogeny | Any ................................................................................................................................... 176
Phylogeny | Bootstrap Test................................................................................................................... 165
Phylogeny | Display Saved Tree Session.Use ...................................................................................... 191
Phylogeny | Maximum Parsimony........................................................................................................ 192
Phylogeny | Minimum Evolution .......................................................................................................... 192
Phylogeny | Neighbor-Joining.............................................................................................................. 164
Phylogeny | UPGMA.This.................................................................................................................... 193
Phylogeny menu ........................................................................................................................... 184, 190
Poisson ................................................................................................................................. 142, 144, 204
Poisson Correction distance ................................................................................................................. 142
Poisson Correction Failed..................................................................................................................... 204
Polypeptide........................................................................................................................................... 213
Position........................................................................................................................................... 95, 113
codon ..................................................................................................................................................95
Preface....................................................................................................................................................15
Print .............................................................................................................................................. 110, 186
Print dialog ........................................................................................................................................... 177
Printer Setup ................................................................................................................................. 177, 187
Program ..................................................................................................................................................19
uncompress.........................................................................................................................................19
Proline ....................................................................................................................................................66
Protein parsimony................................................................................................................................. 214
Psuedorandom number generator ......................................................................................................... 227
Purdom et al. 2000................................................................................................................................ 222
Pyrimindine ............................................................................................................................................65
Q
Query Databanks.................................................................................................................................. 195
Quit Data Viewer.............................................................................................................................. 98, 99
232
Molecular Evolutionary Genetics Analysis
Quit Options dialog .............................................................................................................................. 108
Quit Viewer .................................................................................................................................. 107, 176
R
RAM.......................................................................................................................................................19
Rate....................................................................................................................................................... 118
Read........................................................................................................................................................73
DNA ...................................................................................................................................................74
Relative Rate................................................................................................................................. 171, 172
Relative Rate Tests ............................................................................................................................... 191
Removing ............................................................................................................................................. 198
taxon ......................................................................................................................................... 197, 198
Reopen Data ......................................................................................................................................... 186
Replace ................................................................................................................................................. 112
Reporting Bugs.......................................................................................................................................30
Resampled dataset ................................................................................................................................ 163
Resampling........................................................................................................................... 165, 167, 194
Residue-by-residue ............................................................................................................................... 116
Restore Input Order .............................................................................................................................. 100
RNA ............................................................................................................................................... 66, 187
RSCU ................................................................................................................................................... 206
Rules................................................................................................................................................. 63, 64
Description Statement ........................................................................................................................64
Format Statement ...............................................................................................................................64
Taxa Names........................................................................................................................................63
Title Statement ...................................................................................................................................63
S
Save .............................................................................................................................................. 110, 187
Save As................................................................................................................................................. 110
Save As dialog...................................................................................................................... 110, 177, 178
SBL....................................................................................................................................................... 177
Scale Bar tab......................................................................................................................................... 181
Scrollbar .................................................................................................................................................95
233
Search | Find ......................................................................................................................................... 112
Search | Find Again .............................................................................................................................. 112
Search | Replace ................................................................................................................................... 112
Search menu ...........................................................................................................................................46
Search Menu in Alignment Explorer......................................................................................................60
Select .............................................................................................................................. 95, 106, 113, 198
taxa ..................................................................................................................................... 96, 106, 107
taxon ................................................................................................................................................. 197
Select & Edit Taxa/Groups................................................................................................................... 107
Select Distance Options Dialog............................................................................................................ 228
Select Genetic Code dialog ....................................................................................................................97
Select Genetic Code Table ............................................................................................................... 94, 98
Select Genetic Code Table Dialog........................................................................................................ 200
Select Preferences ................................................................................................................................ 188
Select/Edit Taxa Groups....................................................................................................................... 103
Select/Edit Taxa/Groups window......................................................................................................... 197
Selected Sequences............................................................................................................................... 100
Selection ....................................................................................................................... 167, 168, 183, 194
Fisher's Exact Test............................................................................................................................ 194
Large Sample Tests .......................................................................................................................... 167
Tests | Codon-based Tests ................................................................................................................ 194
Z-Test ............................................................................................................................................... 194
Selection Menu..................................................................................................................................... 194
Sequence Data ........................................................................................................................ 64, 166, 196
Sequence Data Explorer ............................................................................. 97, 98, 99, 103, 104, 183, 184
Sequence Data Organizer .......................................................................................................................97
Sequence Data Subset Selection........................................................................................................... 113
Sequence Diversity submenu................................................................................................................ 189
Sequence Names................................................................................................................................... 103
Sequencer Menu in Alignment Explorer ................................................................................................60
Sequences/groups ......................................................................................................................... 106, 174
Setup/Select Genes ................................................................................................... 98, 99, 105, 112, 198
234
Molecular Evolutionary Genetics Analysis
Setup/Select Genes/Domain ...................................................................................................................67
Setup/Select Taxa ............................................................................................................... 69, 70, 98, 197
Show..................................................................................................................................... 106, 174, 181
pairwise ............................................................................................................................ 106, 174, 175
statistics/frequency ........................................................................................................................... 181
Show Analysis Description .................................................................................................................. 176
Show Group Names.............................................................................................................. 102, 107, 175
Show Information................................................................................................................................. 177
Show Input Data Title .......................................................................................................................... 176
Show Names......................................................................................................................................... 175
Show Only Selected Sequences............................................................................................................ 100
Show Only Selected Taxa .................................................................................................................... 107
Show Pair Name ................................................................................................................................... 175
Show Sequence Names......................................................................................................................... 102
Show Web Browser............................................................................................................................... 195
Show/Hide ............................................................................................................................................ 180
Simple 2-fold........................................................................................................................................ 153
Site Labels ............................................................................................................................................ 200
Site Picker dialog.................................................................................................................................. 198
Sites .................................................................................................. 96, 97, 146, 147, 161, 165, 166, 183
Highlighting........................................................................................................................................95
Number..................................................................................................................................... 146, 147
Sites Redundancy .................................................................................................................................93
Sizer button................................................................................................................................... 107, 174
SoftWindows95 ......................................................................................................................................19
SoftWindows98 ......................................................................................................................................19
Sort 197
Sort Sequences ..................................................................................................................................... 102
Sort Sequences As per Taxa/Group Organizer ..................................................................................... 103
Sort Sequences By Sequence Name ..................................................................................................... 103
Sort Taxa ...................................................................................................................................... 107, 175
Special Symbols .....................................................................................................................................64
235
SQRT............................................................................................................................................ 167, 194
Staden ................................................................................................................................................... 215
Statistical Attributes ...............................................................................................................................93
Computing ..........................................................................................................................................93
Statistics ............................................................................................................................................... 183
Computing ........................................................................................................................................ 183
Statistics | Amino.................................................................................................................................. 105
Statistics | Codon Usage ....................................................................................................................... 105
Statistics | Nucleotide Composition...................................................................................................... 105
Statistics | Nucleotide Pair Frequencies................................................................................................ 105
Statistics | Use....................................................................................................................................... 106
Statistics | Use All Selected Sites ......................................................................................................... 105
Statistics Menu ............................................................................................................................. 104, 183
Statistics/frequency .............................................................................................................................. 181
Status Bar ..................................................................................................................... 45, 46, 96, 97, 107
Subpopulations ..................................................................................................................................... 189
Substitution........................................................................................................................................... 183
Subtree Drawing Options (in Tree Explorer) ....................................................................................... 179
Subtree Menu ....................................................................................................................................... 178
Subtree Option...................................................................................................................................... 181
Sun Workstation .....................................................................................................................................19
Swofford 1998...................................................................................................................................... 223
Swofford et al. 1996 ............................................................................................................................. 223
Synonymous-nonsynonymous.............................................................................................................. 114
Syonymous ................................................................................................................................... 167, 194
System Requirements .............................................................................................................................19
T
Tajima................................................................................................................................... 119, 171, 172
Tajima 1989.......................................................................................................................................... 223
Tajima and Nei 1982 ............................................................................................................................ 223
Tajima Nei distance Gamma rates........................................................................................................ 128
Tajima Nei Distance Gamma Rates and Heterogeneous patterns......................................................... 137
236
Molecular Evolutionary Genetics Analysis
Tajima Nei Distance Heterogeneous patterns....................................................................................... 132
Tajima-Nei.................................................................................................................................... 119, 204
Tajima-Nei distance.............................................................................................................................. 119
Tajima-Nei Distance Could Not Be Computed .................................................................................... 204
Tajima's Test ................................................................................................................................ 171, 172
Neutrality.......................................................................................................................................... 171
Takahashi and Nei 2000 ....................................................................................................................... 223
Takezaki et al. 1995.............................................................................................................................. 224
Tamura ................................................................................................................................................. 204
Tamura 3 parameter Gamma rates and Heterogeneous patterns........................................................... 139
Tamura 3 parameter Heterogeneous patterns ....................................................................................... 133
Tamura 3-parameter distance ............................................................................................................... 121
Tamura 3-parameter Gamma................................................................................................................ 131
Tamura and Kumar 2002...................................................................................................................... 224
Tamura et al. 2004................................................................................................................................ 224
Tamura-Nei .......................................................................................................................... 123, 129, 204
Tamura-Nei distance .................................................................................................................... 123, 129
Tamura-Nei Distance Could Not Be Computed ................................................................................... 204
Tamura-Nei distance Gamma rates and Heterogeneous patterns ......................................................... 138
Tamura-Nei distance Heterogeneous Patterns...................................................................................... 134
Tamura-Nei gamma distance................................................................................................................ 129
Taxa63, 69, 70, 71, 95, 98, 106, 107, 108, 113, 159, 164, 174, 175, 176, 180, 189, 190, 191, 197, 203, 210
Adding.............................................................................................................................................. 198
categorize.......................................................................................................................................... 107
defining...............................................................................................................................................70
Defining Groups .................................................................................................................................69
following ............................................................................................................................................69
Groups .................................................................................... 69, 96, 98, 106, 107, 108, 175, 189, 190
hiding................................................................................................................................................ 176
listing................................................................................................................................................ 197
name ................................................................................................................................................. 175
number...................................................................................................................... 158, 164, 174, 191
237
order ......................................................................................................................................... 180, 197
selecting................................................................................................................................ 95, 96, 106
Taxa Names............................................................................................................................................63
Rules...................................................................................................................................................63
Taxa/Group Organizer.......................................................................................................................... 103
Taxa/Groups ......................................................................................................................................... 197
Taxon...................................................................................................... 63, 107, 174, 176, 180, 181, 197
including........................................................................................................................................... 180
indicate ............................................................................................................................................. 197
manipulate ........................................................................................................................................ 181
Removing ......................................................................................................................................... 197
select................................................................................................................................................. 197
Taxon Iabel.............................................................................................................................................63
Taxon Name tab ................................................................................................................................... 181
Technical Support...................................................................................................................................30
Test of Positive Selection .......................................................................................................................39
Tests | Codon-based Tests .................................................................................................................... 194
Selection ........................................................................................................................................... 194
Tests | Interior Branch Test .................................................................................................................. 164
Tests | Relative Rate Tests............................................................................................................ 171, 191
Tests | Tajima's Test ............................................................................................................................. 171
Neutrality.......................................................................................................................................... 171
Tests menu.................................................................................................................................... 184, 194
Tests of the Reliability of a Tree Obtained.............................................................................................37
Text Editor.................................................................................................................... 109, 110, 111, 112
Text File Editor ......................................................................................................................................45
Text Label............................................................................................................................................. 181
Threonine ...............................................................................................................................................66
Title ............................................................................................................................................ 62, 63, 64
Title Statement .......................................................................................................................................63
Rules...................................................................................................................................................63
Toolbars in Alignment Explorer.............................................................................................................55
238
Molecular Evolutionary Genetics Analysis
Topological distance............................................................................................................................. 216
Trace Data File Viewer/Editor ...............................................................................................................47
Transition/transversion ................................................. 114, 116, 117, 120, 121, 123, 127, 129, 147, 170
Transitions + Transversions ................................................. 116, 117, 118, 120, 121, 123, 126, 127, 129
Translate/Untranslate..............................................................................................................................98
Transversional ...............................117, 118, 120, 121, 122, 123, 124, 127, 129, 130, 149, 151, 153, 154
Transversions................................................................................................ 117, 120, 122, 123, 127, 129
Tree....................................................................................................................................................... 205
Bifurcating........................................................................................................................................ 205
Tree Data .......................................................................................................................................... 64, 73
Tree Explorer........................................................................................................ 176, 177, 178, 180, 182
Tree Explorer window.......................................................................................................................... 177
Tree Length .......................................................................................................................................... 193
Tree tab................................................................................................................................................. 181
Tree/Branch Style................................................................................................................................. 180
Treelength............................................................................................................................................. 177
Tryptophan .............................................................................................................................................66
Txt 31, 62
U
Uncompress ............................................................................................................................................19
program ..............................................................................................................................................19
Undo ..................................................................................................................................................... 111
Unexpected Error ................................................................................................................................. 205
Ungrouped Taxa ................................................................................................................................... 197
Ungrouped Taxa window ..................................................................................................................... 197
Unhide .................................................................................................................................................. 197
Uninstall MEGA 4..................................................................................................................................19
Unique ASCII....................................................................................................................................... 202
Unrooted....................................................................................................... 157, 158, 165, 191, 192, 216
Updates...................................................................................................................................................30
UPGMA ....................................................................................................................................... 164, 193
UPGMA Construct Phylogeny ............................................................................................................. 193
239
Use All Selected Sites .......................................................................................................................... 105
Use Identical Symbol ........................................................................................................................... 101
Use only Highlighted Sites ................................................................................................................... 106
User Stopped Computation................................................................................................................... 205
User-Entered Text ..................................................................................................................................31
Using MEGA in the classroom...............................................................................................................30
V
Valine .....................................................................................................................................................66
Vertebrate mitochondrial........................................................................................................................91
View .......................................................................................................................................................94
View menu ................................................................................................................................... 180, 184
View/Edit Sequencer Files.................................................................................................................... 195
VirtualPC................................................................................................................................................19
W
Web Browser.................................................................................................................................... 48, 49
Web Explorer Tab Alignment Explorer .................................................................................................48
Web Menu in Alignment Explorer .........................................................................................................61
Website............................................................................................................................................. 19, 30
What s New in Version 3.0.....................................................................................................................20
Windows........................................................................................................................................... 16, 19
Windows Clipboard.............................................................................................................................. 111
WinZip ...................................................................................................................................................19
WordPad.................................................................................................................................................62
WordPerfect............................................................................................................................................62
Words .....................................................................................................................................................63
Working With Genes and Domains........................................................................................................38
Write Data ............................................................................................................................................ 186
Writing ............................................................................................................................... 62, 67, 69, 184
Command Statements................................................................................................................... 67, 69
only 4-fold degenerate sites.............................................................................................................. 184
Writing site .............................................................................................................................................99
Y
240
Molecular Evolutionary Genetics Analysis
Yang 1997 ............................................................................................................................................ 224
Yang 1999 ............................................................................................................................................ 224
Yeast mitochondrial................................................................................................................................91
Z
Zhang and Gu 1998 .............................................................................................................................. 225
ZIP file....................................................................................................................................................19
Z-statistic .............................................................................................................................................. 183
Z-Test ........................................................................................................................... 167, 169, 183, 194
conduct ..................................................................................................................................... 167, 194
Selection ........................................................................................................................................... 194
Zuckerkandl and Pauling 1965 ............................................................................................................. 225
241
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