Molecular Evolutionary Genetics Analysis

Molecular Evolutionary Genetics Analysis
Molecular Evolutionary Genetics Analysis
Table of Contents
Molecular Evolutionary Genetics Analysis.................................................................................1
Table of Contents........................................................................................................................ i
preface....................................................................................................................................... 1
Copyright................................................................................................................................ 1
Disclaimer.............................................................................................................................. 2
Preface................................................................................................................................... 3
Acknowledgements................................................................................................................ 4
MEGA Software Development Team.....................................................................................5
Citing MEGA in Publications..................................................................................................6
Part I: Getting Started................................................................................................................ 7
Installing MEGA..................................................................................................................... 7
Features & Support................................................................................................................ 9
A Walk Through MEGA........................................................................................................25
Part II: Assembling Data for Analysis.......................................................................................47
Text File Editor and Format Converter.................................................................................47
Trace Data File Viewer/Editor...............................................................................................49
Web Browser & Data Miner..................................................................................................50
Some Text Editor Utilities.....................................................................................................50
Building Sequence Alignments.............................................................................................54
Part III: Input Data Types and File Format..............................................................................72
MEGA Input Data Formats...................................................................................................72
Importing Data from other Formats......................................................................................90
Genetic Code Tables..........................................................................................................112
Viewing and Exploring Input Data......................................................................................118
Text File Editor and Format Converter...............................................................................159
Visual Tools for Data Management....................................................................................179
Part IV: Evolutionary Analysis...............................................................................................182
Computing Basic Statistical Quantities for Sequence Data................................................182
Computing Evolutionary Distances.....................................................................................186
Constructing Phylogenetic Trees........................................................................................239
Tests of Selection............................................................................................................... 255
Part V: Visualizing and Exploring Data and Results...............................................................262
Distance Matrix Explorer....................................................................................................262
Sequence Data Explorer....................................................................................................266
Tree Explorer..................................................................................................................... 302
Alignment Explorer............................................................................................................. 316
Appendix A: Frequently Asked Questions..............................................................................330
i
Table of Contents
How do I prevent the "MEGA Update Available" message showing up..............................330
How can I ignore the current update available messag in MEGA's main window...............331
Computing statistics on only highlighted sites in Data Explorer..........................................332
Finding the number of sites in pairwise comparisons.........................................................333
Get more information about the codon based Z-test for selection......................................334
Menus in MEGA are so short; where are all the options?...................................................335
Writing only 4-fold degenerate sites to an output file..........................................................336
Appendix B: Main Menu Items and Dialogs Reference..........................................................337
Main MEGA Menus............................................................................................................ 337
MEGA Dialogs.................................................................................................................... 374
Appendix C: Error Messages.................................................................................................381
Blank Names Are Not Permitted.........................................................................................381
Data File Parsing Error.......................................................................................................382
Dayhoff/JTT Distance Could Not Be Computed.................................................................383
Domains Cannot Overlap...................................................................................................384
Equal Input Correction Failed.............................................................................................385
Fisher's Exact Test Has Failed...........................................................................................386
Gamma Distance Failed Because p > 0.99........................................................................387
Gene Names Must Be Unique............................................................................................388
Inapplicable Computation Requested.................................................................................389
Incorrect Command Used..................................................................................................390
Invalid special symbol in molecular sequences..................................................................391
Jukes-Cantor Distance Failed............................................................................................392
Kimura Distance Failed......................................................................................................393
LogDet Distance Could Not Be Computed.........................................................................394
Missing data or invalid distances in the matrix....................................................................395
No Common Sites.............................................................................................................. 396
Not Enough Groups Selected.............................................................................................397
Not Enough Taxa Selected.................................................................................................398
Not Yet Implemented..........................................................................................................399
p distance is found to be > 1..............................................................................................400
Poisson Correction Failed because p > 0.99......................................................................401
Tajima-Nei Distance Could Not Be Computed....................................................................402
Tamura (1992) Distance Could Not Be Computed.............................................................403
Tamura-Nei Distance Could Not Be Computed..................................................................404
Unexpected Error............................................................................................................... 405
User Stopped Computation................................................................................................406
Glossary................................................................................................................................ 407
ABI File Format.................................................................................................................. 407
ii
Table of Contents
Alignment Gaps.................................................................................................................. 408
Alignment session.............................................................................................................. 409
Bifurcating Tree.................................................................................................................. 410
Branch................................................................................................................................ 411
ClustalW............................................................................................................................. 414
Codon................................................................................................................................ 415
Codon Usage..................................................................................................................... 416
Complete-Deletion Option..................................................................................................417
Composition Distance........................................................................................................ 418
Compress/Uncompress......................................................................................................419
Condensed Tree................................................................................................................ 420
Constant Site...................................................................................................................... 422
Degeneracy........................................................................................................................ 423
Disparity Index................................................................................................................... 424
Domains............................................................................................................................. 425
Exon................................................................................................................................... 426
Extant Taxa........................................................................................................................ 427
Flip..................................................................................................................................... 428
Format command............................................................................................................... 429
Gamma parameter............................................................................................................. 430
Gene.................................................................................................................................. 431
Indels................................................................................................................................. 434
Independent Sites.............................................................................................................. 435
Intron.................................................................................................................................. 437
Maximum Composite Likelihood.........................................................................................439
Max-mini branch-and-bound search...................................................................................440
Maximum Parsimony Principle...........................................................................................441
Mid-point rooting................................................................................................................ 442
Monophyletic...................................................................................................................... 444
mRNA................................................................................................................................ 445
NCBI.................................................................................................................................. 446
Newick Format................................................................................................................... 447
Node.................................................................................................................................. 448
Nonsynonymous change....................................................................................................449
Nucleotide Pair Frequencies..............................................................................................450
OLS branch length estimates.............................................................................................451
Orthologous Genes............................................................................................................ 452
Out-group........................................................................................................................... 453
Pairwise-deletion option.....................................................................................................454
iii
Table of Contents
Parsimony-informative site.................................................................................................455
Polypeptide........................................................................................................................ 456
Positive selection............................................................................................................... 457
Protein parsimony.............................................................................................................. 458
Purifying selection.............................................................................................................. 459
Purines............................................................................................................................... 460
Pyrimidines......................................................................................................................... 461
Random addition trees.......................................................................................................462
RSCU................................................................................................................................. 464
Singleton Sites................................................................................................................... 465
Staden................................................................................................................................ 467
Statements in input files.....................................................................................................468
Swap.................................................................................................................................. 469
Synonymous change.......................................................................................................... 470
Taxa................................................................................................................................... 471
Topological distance........................................................................................................... 472
Topology............................................................................................................................ 473
Transition........................................................................................................................... 474
Transition Matrix................................................................................................................. 475
Transition/Transversion Ratio (R).......................................................................................476
Translation......................................................................................................................... 477
Transversion...................................................................................................................... 478
Unrooted tree..................................................................................................................... 480
Variable site....................................................................................................................... 481
References............................................................................................................................ 482
Comeron 1995................................................................................................................... 482
Dopazo 1994...................................................................................................................... 483
Dayhoff 1978...................................................................................................................... 484
Dayhoff 1979...................................................................................................................... 485
DeBry 1992........................................................................................................................ 486
Eck and Dayhoff 1966........................................................................................................487
Efron 1982.......................................................................................................................... 488
Estabrook et al. 1975......................................................................................................... 489
Felsenstein 1978................................................................................................................ 490
Felsenstein 1985................................................................................................................ 491
Felsenstein 1986................................................................................................................ 492
Felsenstein 1988................................................................................................................ 493
Felsenstein 1993................................................................................................................ 494
Felsenstein and Kishino 1993............................................................................................495
iv
Table of Contents
Fitch 1971.......................................................................................................................... 496
Fitch and Margoliash 1967.................................................................................................497
Goldman 1993.................................................................................................................... 498
Gu and Zhang 1997........................................................................................................... 499
Hedges et al. 1992............................................................................................................. 500
Hendy and Penny 1982......................................................................................................501
Hendy and Penny 1989......................................................................................................502
Hillis and Bull 1993............................................................................................................. 503
Hillis et al. 1996.................................................................................................................. 504
Jones et al. 1992................................................................................................................ 505
Jukes and Cantor 1969......................................................................................................506
Kimura 1980....................................................................................................................... 507
Kishino and Hasegawa 1989..............................................................................................508
Kumar et al. 1993............................................................................................................... 509
Kumar and Gadagkar 2001................................................................................................510
Lake 1987.......................................................................................................................... 511
Li 1993............................................................................................................................... 512
Li 1997............................................................................................................................... 513
Li et al. 1985....................................................................................................................... 514
Maddison and Maddison 1992...........................................................................................515
Nei 1986............................................................................................................................. 516
Nei and Gojobori 1986.......................................................................................................517
Nei and Jin 1989................................................................................................................ 518
Nei and Kumar 2000.......................................................................................................... 519
Nei et al. 1976.................................................................................................................... 520
Nei et al. 1985.................................................................................................................... 521
Nei et al. 1998.................................................................................................................... 522
Page and Holmes 1998......................................................................................................523
Pamilo and Bianchi 1993....................................................................................................524
Pamilo and Nei 1988.......................................................................................................... 525
Penny and Hendy 1985......................................................................................................526
Press et al. 1993................................................................................................................ 527
Purdom et al. 2000............................................................................................................. 528
Rzhetsky and Nei 1992......................................................................................................529
Rzhetsky and Nei 1993......................................................................................................530
Saitou and Nei 1987........................................................................................................... 531
Sankoff and Cedergren 1983.............................................................................................532
Sharp et al. 1986................................................................................................................ 533
Sneath and Sokal 1973......................................................................................................534
v
Table of Contents
Sourdis and Krimbas 1987.................................................................................................535
Sourdis and Nei 1988.........................................................................................................536
Studier and Keppler 1988...................................................................................................537
Swofford 1993.................................................................................................................... 538
Swofford 1998.................................................................................................................... 539
Swofford et al. 1996........................................................................................................... 540
Tajima 1983....................................................................................................................... 541
Tajima 1989....................................................................................................................... 542
Tajima 1993....................................................................................................................... 543
Tajima and Nei 1982.......................................................................................................... 544
Tajima and Nei 1984.......................................................................................................... 545
Takahashi and Nei 2000....................................................................................................546
Takezaki et al. 1995........................................................................................................... 547
Tamura 1992...................................................................................................................... 548
Tamura 1994...................................................................................................................... 549
Tamura and Kumar 2002...................................................................................................550
Tamura and Nei 1993.........................................................................................................551
Tanaka and Nei 1989......................................................................................................... 552
Tateno et al. 1982.............................................................................................................. 553
Tateno et al. 1994.............................................................................................................. 554
Yang 1999.......................................................................................................................... 555
Zhang and Gu 1998........................................................................................................... 556
Zhang et al. 1997............................................................................................................... 557
Zhang et al. 1998............................................................................................................... 558
Zuckerkandl and Pauling 1965...........................................................................................559
Tamura et al. 2007............................................................................................................. 560
Glossary................................................................................................................................ 561
Index...................................................................................................................................... 562
vi
preface
Copyright
Copyright © 1993 - 2009.
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
Molecular Evolutionary Genetics Analysis
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.
2
preface
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 a graphical interface of the late 1990’s, fulfilling the fastgrowing 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 5 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 5
is distinct from previous versions, we have made a special effort to retain the userfriendly interface that researchers have come to identify with MEGA. We have
simplified the file activation process, where you may select an analysis before needing
to open a file.
3
Molecular Evolutionary Genetics Analysis
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 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 currently supported by research grants from the
National Institutes of Health.
4
preface
MEGA Software Development Team
Project Directors and Principal Programmers
Sudhir Kumar and Koichiro Tamura
Associate Programmers
Daniel Peterson, Nicholas Peterson, and Glen Stecher
Website Manager and Designs
Linwei Wu and Wayne Parkhurst
Quality Assurance
Linwei Wu and the MEGA team
See also Acknowledgements.
5
Molecular Evolutionary Genetics Analysis
Citing MEGA in Publications
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 5 (Tamura, Peterson, Stecher, Nei, and Kumar 2011).
(2) When including a MEGA citation in the Literature Cited/Bibliography section, you may use
the following:
Koichiro Tamura, Daniel Peterson, Nicholas Peterson, Glen Stecher, Masatoshi Nei,
and Sudhir Kumar (2011) MEGA5: Molecular Evolutionary Genetics Analysis using Likelihood, Distance, and
Parsimony methods. Molecular Biology and Evolution.
(Publication PDF at http://www.kumarlab.net/publications)
6
Part I: Getting Started
Installing MEGA
System Requirements
MEGA was developed for use on Microsoft Windows® operating systems: Windows
95/98, NT, ME, 2000, XP, or later. The minimum computer requirements are at least
64 MB of RAM and 20 MB of available hard disk space with a Pentium® processor.
The more RAM and faster your CPU (processor) is, the faster an analysis will finish.
MEGA also can be run on other operating systems for which Windows emulators are
available.
Platform
Software
Macintosh
(<=10.4)
Windows using VirtualPC
Macintosh(>=10.5)
Normal OSX install
Sun Workstation
SoftWindows95
Linux
Windows using VMWare
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Molecular Evolutionary Genetics Analysis
Installing MEGA
The preferred way to install MEGA is directly from the website
(www.megasoftware.net). A specially designed installation program automatically
downloads MEGA and installs it in the location (directory) you specify.
If you are using Windows Vista and are experiencing problems during instillation then
please disable the UAC (User Account Control). This can be done through Control
Pannel -> User Accounts -> Change Security Settings -> Deselect the checkbox (Use
user account control (UAC)) and click ok.
If you are unable to install MEGA 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 on your computer automatically.
Finally, you may install MEGA 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 in one of the three ways described above.
Please do not simply copy MEGA-related files from one computer to another, as
MEGA may not work properly if installed in this manner.
8
Part I: Getting Started
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 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, scroll down to MEGA so
that it is highlighted, then click Add/Remove.
The preferred way to uninstall in Mac OSX is to simply drag the program from your
applications folder to the trash bin.
Features & Support
What's New in Version 5
Version 5 contains a number of enhancements over MEGA 4. They include
Maximum likelihood analyses
Session saving
Saves and restores any settings set such as groups, selections, etc. It is also
MUCH faster at saving and restoring than exporting to MEGA format or reading in from
MEGA format.
Codon by Codon estimation
Support added from the HyPhy project.
Partial Deletion
Providing user Trees
For some analyses you may provide a pre-computed tree, and analyze trees.
MUSCLE
Muscle multiple sequence alignment can be used through MEGA.
Export to Excel and CSV
MEGA can now Export to the Excel format from Sequence Viewer, Distance
Viewer, Matrix results viewer, and various other places where statistics used to be
shown as plain text in the Text Editor.
Update Notification
You may check whether there is a new version of MEGA available by pressing
the “Check for Update”, button on Mega’s main form.
Tree Topology Editor
9
Molecular Evolutionary Genetics Analysis
This functionality allows you to load a tree into MEGA and edit the tree’s
topology. Some of the available functions are, deleting/adding branches, swapping,
rooting, etc.
10
Part I: Getting Started
Feature List
MEGA Version
1.0
2.x
3.x
4.x
Platform
DOS
Win
Win
Win
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Manual editing of DNA and
Protein sequences
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Motif searching/highlighting
•
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Synchronous alignment editing of
original and translated cDNA
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Copy/Paste sequences To/From
Clipboard
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Save alignment session for future
display
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Ability to read sequencer, MEGA,
NEXUS, FASTA, and other formats
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Apply color/highlight schemes to
sequence data
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Write alignment to MEGA file for
direct analysis in MEGA
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BLAST sequences from alignment
directly
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Input Data
DNA, Protein, Pairwise distance
matrix
Sequence Alignment Construction
Alignment Editor
Multiple Sequence Alignment
Complete native implementation
of ClustalW
Ability to select all options on the
fly
11
Molecular Evolutionary Genetics Analysis
Ability to align any user-selected
region
•
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Ability to align translated cDNA
sequences and automatic adjustment
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Edit trace file
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Mask vector (or any other region)
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Launch direct BLAST search for
whole or selected sequence
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Send data directly to Alignment
Editor
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Direct "usual" web and GenBank
browsing from MEGA
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One-click sequence fetching from
databanks queries
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Send sequence data from BLAST
search directly into alignment
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Bookmark favorite sequence
databank sites
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Sequencer (Trace) File editor/viewer
View ABI (*.abi, .ab1) and
Studfen (*.std?)
Integrated Web Browser and Sequence Fetching
Data Handling
Handling ambiguous states (R,Y,T,
etc.)
•
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Extended MEGA format to save all
data attributes
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Importing Data from other formats
(Clustal/Nexus/etc.)
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Data Explorers
Sequence
12
•
Part I: Getting Started
Distance matrix
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Genes and Mixed Domain
attributes
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Explicit labels for sites
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Attributes supported
Groups of Sequences/Taxa
Domains
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Automatic codon translation
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Selection of codon positions
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Selection of different site
categories
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Visual Specification of
Domains/Groups
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Center Analysis Preferences Dialog
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
•
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Numbers of potential synonymous
sites
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Inclusion of all known code tables
•
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Real-Time Caption Expert Engine
Generate Captions for Distance
Matrices
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13
Molecular Evolutionary Genetics Analysis
Generate Captions for Phylogenies
•
Generate Captions for Tests
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Generate Captions for Alignments
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Copy Captions to External Programs
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Save/Print Captions
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Integrated Text File Editor
Unlimited Text File Size
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Multi-file Tabbed Display
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Columnar Block selection/Editing
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Undo/Redo operations
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Line numbers
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Utilities to Format Sequences/Reverse
complement etc.
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Copy Screenshots to
EMF/WMF/Bitmap for presentation
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Display with identity symbol
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Drag-drop sorting of sequences
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Mixing coding and non-coding
sequence display
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Sequence Data Viewer
Two dimensional display of molecular
sequences
One-click translation
Display with all or only selected taxa
Data Export
14
•
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Part I: Getting Started
Nexus
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Excel
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CSV
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PAUP3, PHYLIP
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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
•
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Transition/Transversion Rate Ratios
(k1, k2)
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Transition/Transversion Rate Bias (R)
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Substitution Pattern Homogeneity Test
Composition Distance
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Disparity Index
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Monte-Carlo Test
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15
Molecular Evolutionary Genetics Analysis
Distance Estimation Methods
Nucleotide-by-Nucleotide
Models
No. of differences, pdistance, Jukes-Cantor, Kimura 2P
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Tajima-Nei, Tamura 3parameter, Tamura-Nei distance
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LogDet (Tamura-Kumar)
Maximum Composite Likelihood
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Subcomponents
Transitions (ts), tranversions
(tv), ts/tv ratio
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Modified Nei-Gojobori method
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Li-Wu-Lou, PBL, Kumar method
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Numbers of synonymous
and nonsynonymous sites
•
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Differences and ratios (s-n,
n-s, s/n, n/s)
•
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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
•
Subcomponents
Synonymous (s),
nonsynonymous (n) distances
16
•
Part I: Getting Started
4-fold degenerate site
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Between Group Average
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Within Group Average
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Net between group Average
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Overall average
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Mean Diversity within
Subpopulations
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Mean Diversity for Entire
Population
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Mean Interpopulational Diversity
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Coefficient of Differentiation
•
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distances
0-fold degenerate site
distances
Number of 0-fold and 4-fold
degenerate sites
Protein distance
Number of differences, p-distance,
Poisson
•
Dayhoff and JTT distances
Account for rate variation among
sites
•
Relaxation of the homogeneity
assumption
Distance Calculations
Pairwise
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Sequence Diversity Calculations
Variance Calculations
17
Molecular Evolutionary Genetics Analysis
Analytical
•
Bootstrap
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Handling missing data
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Automatic translation
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Within groups
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Overall sequences
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Fisher's Exact Test
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Tajima's Test of Neutrality
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Randomized tie-breaking in
bootstrapping
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Minimum Evolution method
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Branch-swapping (CloseNeighbor-Interchange; CNI)
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Automatic pasting of partial codons
between exons
Tests of Selection
Codon-based tests
Large sample Z-test
Between Sequences
Molecular Clock Test
Tajima's relative rate test
Tree-making Methods
Neighbor-Joining
•
Fast OLS computation method
UPGMA
18
•
Part I: Getting Started
Randomized tie-breaking in
bootstrapping
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Branch-swapping (CNI)
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Average branch length estimation
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Minimum Evolution
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Maximum Parsimony
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Maximum Parsimony
Nucleotide sequences
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Protein sequences
Max-mini branch-and-bound and
min-mini searches
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Bootstrap Test of Phylogeny
Neighbor-joining/UPGMA
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Confidence Probability Test
Neighbor-joining
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Minimum Evolution
Consensus tree construction
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Condensed tree construction
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View pairwise distances
•
•
•
View between group distances
•
•
•
View within group distances
•
•
•
View distances and standard errors
simultaneously
•
•
•
Sort the distance matrix
•
•
•
Distance Matrix Viewer
19
Molecular Evolutionary Genetics Analysis
Drag-and-drop
•
•
•
Group-wise
•
•
•
By Sequence names
•
•
•
Control display precision
•
•
•
Export Data for printing or reimporting
•
•
•
•
•
•
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
•
•
•
•
•
•
Tree Explorers
Phylogeny Display and Graphic
printing
•
User specified control for
Placement and precision of branch
length
Ability to view multiple trees in
different viewers
20
Part I: Getting Started
Tree Editing
Flipping, re-rooting
•
•
•
Add marker symbols to names
•
•
•
Multi-color display and printing
•
•
•
Vertical separation between taxa
•
•
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Horizontal size
•
•
•
Change Tree shape
•
•
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Multiple tree display
•
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•
Save tree session for future display
•
•
•
What you see is what you get printing
•
•
•
Multi- or single page printing
•
•
•
•
•
Change Tree Size
Display images on tree for groups and
taxa
21
Molecular Evolutionary Genetics Analysis
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 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.
22
Part I: Getting Started
Technical Support and Updates
All minor (bug fix) and major updates of MEGA will be made available at the website
www.megasoftware.net. You can manually check for a newer version of MEGA by
clicking the “Updates?” button which has a picture of a checkered flag on it, located in
the bottom right of MEGA.
23
Molecular Evolutionary Genetics Analysis
Reporting Bugs
If you encounter technical problems such as unexplained errors, documentation
inconsistencies, or program crashes, please report them to us by clicking the ‘Report a
Bug’ link in MEGA’s main window. Please note that telephone inquiries will not be
accepted.
Please include the following information in your report: (1) your name and email
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 [this often is crucial to
understanding and remedying the problem quickly.]
24
Part I: Getting Started
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
www.megasoftware.net
Pop-up help
links
Dotted
Underlined +
Green
statement
Help Jumps
Underlined +
Green
set of rules
Menu/Screen
Items
Italic
Data Menu
User-Entered
Text
Monospace
font
!Title
A Walk Through MEGA
Introduction to Walk through MEGA
This walk-through provides several brief tutorials that explain how to perform common tasks in
MEGA. Each tutorial requires the use of sample data files which can be found in
the /MEGA/Examples folder (default location for Windows users is C:\Program
Files\MEGA\Examples\. The location for Mac users is $HOME/MEGA/Examples, where
$HOME is the user’s home directory). It is recommended that you follow the examples for a
given tutorial in the order presented as the techniques explained in the initial examples are used
again in the subsequent ones.
In the tutorials, the following conventions are used:
•
•
•
•
•
•
Keystrokes are indicated by bold letters (e.g., F4).
If two keys must be pressed simultaneously, they are shown with a + sign between them
(e.g., Alt + F3 means that the Alt and F3 keys should be pressed at the same time).
Italicized words indicate the name of a menu or window.
Italicized bold words indicate individual commands that are found in menus, submenus,
and toolbars.
‘Main menu’ refers to the menu bar at the top of the currently active window (File,
Analysis, Help, etc.).
‘Main MEGA menu’ refers to the menu on the main window of MEGA where you
launch all of the analyses from.
25
Molecular Evolutionary Genetics Analysis
•
‘Launch bar’ refers to the toolbar located directly below the main menu of the currently
active window (Align, Data, Models, Distance, etc.).
•
For brevity, a sequence of menu / button clicks is indicated by a sequence of commands
separated by pipes (e.g., ‘File | Open’ indicates that you should click on the ‘File’ main
menu item and then click on the ‘Open’ sub menu item that is displayed).
I want to learn about:
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
Mega Basics
Aligning Sequences
Estimating Evolutionary Distances
Building Trees from Sequence Data
Testing Tree Reliability
Working with Genes and Domains
Testing for Selection
Managing Taxa with Groups
Computing Sequence Statistics
Building Trees from Distance Data
Constructing Likelihood Trees
Editing Data Files
26
Part I: Getting Started
Aligning Sequences
In this tutorial, we will show how to create a multiple sequence alignment from protein
sequence data that will be imported into the alignment editor using different methods. All of
the data files used in this tutorial can be found in the MEGA\Examples\ folder (The default
location for Windows users is C:\Program Files\MEGA\Examples\. The location for Mac
users is $HOME/MEGA/Examples, where $HOME is the user’s home directory).
Opening an Alignment
The Alignment Explorer is the tool for building and editing multiple sequence alignments in
MEGA.
Example 2.1:
Launch the Alignment Explorer by selecting the Align | Edit/Build Alignment on the
launch bar of the main MEGA window.
Select Create New Alignment and click Ok. A dialog will appear asking “Are you
building a DNA or Protein sequence alignment?” Click the button labeled “DNA”.
From the Alignment Explorer main menu, select Data | Open | Retrieve sequences
from File. Select the "hsp20.fas" file from the MEG/Examples directory.
Aligning Sequences by ClustalW
You can create a multiple sequence alignment in MEGA using either the ClustalW or Muscle
algorithms. Here we align a set of sequences using the ClustalW option.
Example 2.2:
Select the Edit | Select All menu command to select all sites for every sequence in the
data set.
Select Alignment | Align by ClustalW from the main menu to align the selected
sequences data using the ClustalW algorithm. Click the “Ok” button to accept the
default settings for ClustalW.
Once the alignment is complete, save the current alignment session by selecting Data |
Save Session from the main menu. Give the file an appropriate name, such as
"hsp20_Test.mas". This will allow the current alignment session to be restored for
future editing.
Exit the Alignment Explorer by selecting Data | Exit Aln Explorer from the main menu.
27
Molecular Evolutionary Genetics Analysis
Aligning Sequences Using Muscle
Here we describe how to create a multiple sequence alignment using the Muscle option.
Example 2.3:
Starting from the main MEGA window, select Align | Edit/Build Alignment from the
launch bar. Select Create a new alignment and then select DNA.
From the Alignment Explorer window, select Data | Open | Retrieve sequences from a
file and select the “Chloroplast_Martin.meg” file from the MEGA/Examples directory.
On the Alignment Explorer main menu, select Edit | Select All.
On the Alignment Explorer launch bar, you will find an icon that looks like a flexing
arm. Click on it and select Align DNA.
Near the bottom of the MUSCLE - AppLink window, you will see a row called
Alignment Info. You can scroll through the text to read information about the Muscle
program.
Click on the Compute button (accept the default settings). A Progress window will keep
you informed of Muscle alignment status. In this window, you can click on the
Command Line Output tab to see the command-line parameters which were passed to
the Muscle program. Note: The analysis may complete so fast, that you won’t be able
to click on this tab or read it. The information in this tab isn’t essential, it’s just
interesting.
When the Muscle program has finished, the aligned sequences will be passed back to
MEGA and displayed in the Alignment Explorer window.
Close the Alignment Explorer by selecting Data | Exit Aln Explorer. Select No when
asked if you would like to save the current alignment session to file.
Obtaining Sequence Data from the Internet (GenBank)
Using MEGA’s integrated browser you can fetch GenBank sequence data from the NCBI
website if you have an active internet connection.
Example 2.4:
28
Part I: Getting Started
From the main MEGA window, select Align | Edit/Build Alignment from the main
menu.
When prompted, select Create New Alignment and click ok. Select DNA
Activate MEGA’s integrated browser by selecting Web | Query Genbank from the main
menu.
When the NCBI: Nucleotide site is loaded, enter CFS as a search term into the search
box at the top of the screen. Press the Search button.
When the search results are displayed, check the box next to any item(s) you wish to
import into MEGA.
If you have checked one box: Locate the dropdown menu labeled Display
Settings (located near the top left hand side of the page directly under the tab
headings). Change its value to FASTA and then click Apply. The page will
reload with all the search results in a FASTA format
If you have checked more than one box: locate the Display Settings dropdown
(located near the top left hand side of the page directly under the tab headings).
Change the value to FASTA (Text) and click the Apply button. This will output
all the sequences you selected as a text in the FASTA format.
Press the Add to Alignment button (with the red + sign) located above the web address
bar. This will import the sequences into the Alignment Explorer.
With the data now displayed in the Alignment Explorer, you can close the Web Browser
window.
Align the new data using the steps detailed in the previous examples.
Close the Alignment Explorer window by clicking Data | Exit Aln Explorer. Select No
when asked if you would like the save the current alignment session to file.
Note: We have aligned some sequences and they are now ready to be analyzed. Whenever you
need to edit/change your sequence data, you will need to open it in the Alignment Editor and
edit or align it there. Then export it to the MEGA format and open the resulting file.
29
Molecular Evolutionary Genetics Analysis
Estimating Evolutionary Distances
In this tutorial, we will estimate evolutionary distances for sequences from 11 Drosophila
species using various models. The data files used in this tutorial can be found in the
MEGA/Examples folder (The default location for Windows users is C:\Program
Files\MEGA\Examples. The default location for Mac users is $HOME/MEGA/Examples,
where $HOME is the user’s home directory).
Estimating Evolutionary Distances Using Pairwise Distance
In MEGA, you can estimate evolutionary distances between sequences by computing the
proportion of nucleotide differences between each pair of sequences.
Example 3.1:
Open the "Drosophila_Adh.meg" data file. If needed, refer to the “MEGA Basics”
tutorial.
From the main MEGA launch bar, select Distance | Compute Pairwise Distance.
In the Analysis Preferences window, click the Substitutions Type pull-down and then
select the Nucleotide option.
Click the pull-down for Model/Method and select the p-distance model. For this
example we will be using the defaults for the remaining options. Click Compute to
begin the computation.
A progress indicator will appear briefly and then the distance computation results will
be displayed in grid form in a new window. Leave this window open so we can compare
the results from the next steps.
Compute and Compare Distances Using Other Models/Methods
MEGA supports a wide collection of models for estimating evolutionary distances. Here we
compare evolutionary distances calculated by using different models.
Example 3.2:
Repeat Example 3.1 above, but select the Jukes/Cantor model under the Model/Method
pull-down instead of the p-distance model, leaving all the other options the same.
Again, leave the results window open for comparison.
30
Part I: Getting Started
Repeat the analysis, this time selecting the Tamura-Nei model under the Model/Method
pull-down, leaving all the other options the same. Again, leave the results window open
for comparison.
You are now able to compare the three open result windows which contain the distances
estimated by the different methods.
After you have compared the results, select the File | Quit Viewer option for each result
window. Do not close the "Drosophila_Adh.meg" data file.
Compute the Proportion of Amino Acid Differences
You can also calculate evolutionary distances based on the proportion of amino acid
differences.
Note: MEGA will automatically translate nucleotide sequences into amino acid sequences
using the selected genetic code table. The genetic code table can be edited by Data | Select
Genetic Code Table from the main MEGA launch bar.
Example 3.3:
From the main MEGA window, select Distance | Compute Pairwise Distances from the
main menu. This will display the Analysis Preferences window.
Click the Substitutions Type pull-down, select Amino Acid and then select p-distance
under Model/Method.
Click the Compute button to accept the default values for the rest of the options and
begin the computation. A progress dialog box will appear briefly. As with the
nucleotide estimation, a results viewer window will be displayed, showing the distances
in a grid format.
After you have inspected the results, use the File | Quit Viewer command to close the
results viewer.
Close the data by selecting the Close Data button on the main MEGA task bar.
31
Molecular Evolutionary Genetics Analysis
Building Trees from Sequence Data
In this tutorial, we will illustrate the procedures for building trees and in-memory sequence
data editing, using the commands available in the Data and Phylogeny menus. We will be
using the "Crab_rRNA.meg" file which can be found in the MEGA/Examples directory. This
file contains nucleotide sequences for the large subunit mitochondrial rRNA gene from
different crab species (Cunningham et al. 1992). Since the rRNA gene is transcribed, but not
translated, it falls in the category of non-coding genes.
The “Crab_rRNA.meg” file used in this tutorial can be found in the MEGA/Examples folder
(The default location for Windows users is C:\Program Files\MEGA\Examples. The default
location for Mac users is $HOME/MEGA/Examples, where $HOME is the user’s home
directory).
Building a Neighbor-Joining (NJ) Tree
In this example, we will illustrate the basics of phylogenetic tree re-construction using MEGA
and become familiar with the Tree Explorer window.
Example 4.1:
Activate the "Crab_rRNA.meg" data file. If necessary, refer to Example 1.2 of the
“MEGA Basics” tutorial.
From the main MEGA launch bar, select Phylogeny | Construct/Test Neighbor-Joining
Tree menu option.
In the Analysis Preferences window select the p-distance option from the
Model/Method drop-down.
Click Compute to accept the defaults for the rest of the options and begin the
computation. A progress indicator will appear briefly before the tree displays in the
Tree Explorer window.
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.
Select a branch and then press the Up, Down, Left, and Right arrow keys to see how
the cursor moves through the tree.
Change the branch style by selecting the View | Tree/Branch Style command from the
Tree Explorer main menu.
32
Part I: Getting Started
Select the View | Topology Only command from the Tree Explorer main menu to
display the branching pattern on the screen.
You can display the numerical branch lengths in the Topology Only option by selecting
View | Options and clicking on the Branch tab. Check the box labeled Display Branch
Length and click Ok.
Printing the NJ Tree (For Windows users)
Windows users can print directly from Tree Explorer.
Example 4.2a:
Select the File | Print option from the Tree Explorer main menu to bring up a standard
Print window. This will print the tree full-sized and may take multiple sheets of paper.
Press Cancel.
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 main menu. Press Ok.
Select the File | Exit Tree Explorer command to exit the Tree Explorer. Click the OK
button to close the Tree Explorer without saving the tree session.
Printing the NJ Tree (For Mac users)
MEGA does not support printing directly from Tree Explorer when running on a Mac system.
To print a tree using a Mac, users can save the tree image to a PDF file and then print it by
normal means.
Example 4.2b:
Select the Image | Save as PDF File option from the Tree Explorer main menu to bring
up a standard Save window. Save the image to the desired location.
Once the document is saved, you can open it with your PDF reader and print the
document in the same manner as any other PDF document.
Select the File | Exit Tree Explorer command to exit the Tree Explorer. Click the OK
button to close the Tree Explorer without saving the tree session.
33
Molecular Evolutionary Genetics Analysis
Construct a Maximum Parsimony (MP) Tree Using the Branch-&-Bound Search Option
Using MEGA, you can re-construct a phylogeny using Maximum Likelihood, Minimum
Evolution, UPGMA, and Maximum Parsimony methods in addition to Neighbor-Joining. Here
we re-construct the phylogeny for the “Crab_rRNA.meg” data using the Maximum Parsimony
(MP) method.
Example 4.3
Select the Phylogeny | Construct/Test Maximum Parsimony Tree(s) menu option from
the main MEGA launch bar. In the Analysis Preferences window, choose Max-mini
Branch-&-bound for the MP Search Method option.
Click the Compute 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.
(Windows users) Now print this tree by selecting either of the Print options from the
Tree Explorer's File menu.
(Mac users) Save the tree to a PDF file as described in Example 4.2b above.
Compare the NJ and MP trees. For this data set, the branching pattern of these two trees
is identical.
Select the File | Exit Tree Explorer command to exit the Tree Explorer. Click OK to
close Tree Explorer without saving the tree session.
Constructing a MP Tree using the Heuristic Search
For each method of phylogenetic inference, MEGA provides numerous options. In this
example, we conduct MP analysis using the Min-Mini Heuristic search.
Example 4.4:
Follow the steps in Example 4.3 and instead of choosing Max-mini Branch-&-bound,
choose Min-Mini Heuristic for MP Search Method. Change the MP Search Level to 2
and click Compute.
Note: In this example, the same tree is obtained by the Max-mini Branch-&-bound option as
in the Min-Mini Heuristic option as long as the MP Search Level is set to 2. However, the
computational time is much shorter for the Heuristic method.
34
Part I: Getting Started
Examining Data Editing Features
For noncoding sequence data, OTUs (Operational Taxonomic Units) as well as sites can be
selected for analysis.
Example 4.5:
From the main MEGA window select the Data | Select Taxa and Groups option from
the launch bar. A dialog box is displayed.
All the OTU labels are checked in the left panel. 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 first OTU name in the left panel. Click the Close
button.
Now, when you construct a neighbor-joining tree from this data set, it will contain 12
OTUs instead of 13. Close out of the Tree Explorer window by selecting File | Exit
Tree Explorer and do not save. Deactivate the operational data set by selecting the
Close Data icon from the main MEGA window.
35
Molecular Evolutionary Genetics Analysis
Testing Tree Reliability
In this example, we will conduct two different tests of reliability using protein-coding genes
from the chloroplast genomes of nine different species.
The data file “Chloroplast_Martin.meg” which is used in this tutorial can be found in the
MEGA/Examples folder (The default location for Windows users is C:\Program
Files\MEGA\Examples. The default location for Mac users is $HOME/MEGA/Examples,
where $HOME is the user’s home directory).
Bootstrap Testing for a Neighbor-Joining Tree
Example 5.1:
Activate the "Chloroplast_Martin.meg" file. If necessary, refer to Example 1.2 of
“MEGA Basics”.
On the main MEGA window task bar, select the Phylogeny | Construct/Test NeighborJoining Tree option.
The Analysis Preferences window appears on the screen. For the Model/Method, select
p-distance. Select Bootstrap method for the Test of Phylogeny option.
Click Compute 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.
Once the computation is complete, the Tree Explorer appears and displays two tree tabs.
The first tab is the original tree and the second is the Bootstrap consensus tree.
To produce a condensed tree, use the Compute | Condensed Tree main menu command
from the Tree Explorer window. You can further manipulate the appearance of the
condensed tree here. To change the cutoff value, select the View | Options menu
command and click the Cutoff tab. For now, keep the Cut-off value at 50% and click the
OK button.
This tree shows all the branches that are supported at the default cutoff value of BCL ≥
50. Select the Compute | Condensed Tree main menu command and the original NJ tree
will reappear.
From the Tree Explorer window, select the Image | Save as PDF File option and save a
PDF image of the tree to a convenient location.
36
Part I: Getting Started
From the Tree Explorer window, select the File | Exit Tree Explorer 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.
Interior-branch testing for the Neighbor-Joining Tree
For neighbor-joining trees, you may conduct the standard error test for every interior branch
by using the Interior branch test of phylogeny.
Example 5.2:
From the main MEGA window, select Phylogeny | Construct/Test Neighbor-Joining
Tree from the launch bar.
In the Analysis Preferences dialog, make sure the Substitutions Type option is set to
Amino Acid and the Model/Method is set to p-distance. Set the Test of Phylogeny
option to Interior-branch test.
Click Compute to begin the computation. A progress indicator window will appear
briefly. When the tree appears, confidence probabilities (CP) from the standard error
test of branch lengths are displayed on the screen.
Compare the CP values on this tree with the BCL values of the tree that you saved as a
PDF file in the previous exercise.
Now close the Tree Explorer by selecting File | Exit Tree Explorer from the main
menu. Close the current data by clicking the Close Data icon on the main MEGA
window.
37
Molecular Evolutionary Genetics Analysis
Working With Genes and Domains
Defining and Editing Gene and Domain Definitions
In this example we will demonstrate how to specify coding and non-coding regions of a
sequence. We will be using the file “Contigs.meg” which is located in the MEGA/Examples
directory folder (The default location for Windows users is C:\Program
Files\MEGA\Examples. The default location for Mac users is $HOME/MEGA/Examples,
where $HOME is the user’s home directory).
Example 6.1:
Activate the data file "Contigs.meg". If necessary, refer to Example 1.2 of the “MEGA
Basics” tutorial.
From the main MEGA window launch bar, select Data | Select Genes and Domains.
Notice the column header bar across the top (‘Name’, ‘From’, ‘To’, ‘#Sites’, ‘Coding?’
'Codon Start’). Domains will be listed under the column header labeled ‘Name’. Click
on the domain labeled Data underneath the Genes/Domains group, then click on the
button labeled Delete/Edit. Select Delete Gene/Domain to delete the data domain.
Click on the Genes/Domains label and then click the Add Domain button. Select Add
New Domain from the popup menu.
Right-click on the new domain and select Edit Name from the popup menu. Change the
name to “Exon1” and press the Enter key.
Select the ellipses (…) button next to the first question mark in the ‘From’ column to set
the first site of the domain. When the Start site for Exon1 window appears, select site
number 1 for the AC087512 chimp row and push the Ok button.
Select the ellipsis (…) button in the ‘To’ column to set the last site of the domain. When
the End site for Exon1 window appears, select site number 3918 for the AC087512
chimp row and push the OK button.
Check the box in the ‘Coding?’ column to indicate that this domain is protein coding.
You will need to click the box three times before the check mark appears.
Add two more domains to the Genes/Domains item using the same steps. 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.
38
Part I: Getting Started
Click on the Genes/Domains item to highlight it and then click the Add Gene button at
the bottom of the screen. From the popup menu choose Add new gene at the end. Right
click on this new gene and change the name to “Predicted Gene”. Click and drag all of
the newly created domains to the Predicted Gene so that they now appear under the new
gene.
Press the Close button at the bottom of the window to exit the Gene/Domain
Organization window.
Using Domain Definitions to Compute Pairwise Distances
Now, if we compute pairwise distances between our sequences, the non-coding regions that
we specified in the example above will be ignored.
Example 6.2:
From the main MEGA window, select the Distance | Compute Pairwise Distances
option from the launch bar.
In the Analysis Preferences window, click on the Substitutions Type drop-down and
select Nucleotide. The Select Codon Positions row is now enabled. Make sure that the
Noncoding sites option does not have a checkmark next to it. Click the Compute button
to begin the analysis.
When the computation is complete, the Pairwise Distances window will display the
pairwise distance computed using only the sequence data from exonic domains of the
Predicted Gene. Close the Pairwise Distances window by selecting File | Quit Viewer
and the Sequence Data Explorer window by selecting the Close Data icon on the main
MEGA window.
39
Molecular Evolutionary Genetics Analysis
Testing for Selection
In this example, we describe how to perform a codon-based test of positive selection for five
alleles from the human HLA-A locus (Nei and Hughes 1991).
The “HLA-3Seq.meg" data file, which is used in this tutorial, can be found in the
MEGA/Examples folder (The default location for Windows users is C:\Program
Files\MEGA\Examples. The default location for Mac users is $HOME/MEGA/Examples,
where $HOME is the user’s home directory).
Computing Synonymous and Non-synonymous Distances
Example 7.1:
Activate the "HLA-3Seq.meg" file. If necessary, refer to Example 1.2 in the “MEGA
Basics” tutorial.
From the main MEGA window launch bar, select Selection | Codon-based Z-Test of
Selection.
An Analysis Preferences window appears. For the Model/Method, select the NeiGojobori method (Proportion) model.
In the Test Hypothesis (HA: alternative) row, select Positive Selection (HA: dN > dS)
from the pull-down menu.
From the Scope row, select the Overall Average option.
For the Gaps/Missing Data Treatment option, select Pairwise Deletion.
Click on "Compute" to accept the default values for the remaining options. A progress
indicator appears briefly, and then the computation results are displayed in a results
window in grid format.
The column labeled "Prob" contains the probability computed (must be <0.05 for
hypothesis rejection at 5% level). The column labeled "Stat" contains the statistic used
to compute the probability. The difference in synonymous and non-synonymous
substitutions should be significant at the 5% level.
Close the Test of Positive Selection window.
40
Part I: Getting Started
Managing Taxa with Groups
The “Crab_rRNA.meg” file, which is used in this tutorial, can be found in the
MEGA/Examples folder (The default location for Windows users is C:\Program
Files\MEGA\Examples. The default location for Mac users is $HOME/MEGA/Examples,
where $HOME is the user’s home directory).
Defining and Editing Groups of Taxa
In MEGA, you can partition data into distinct groups and then evaluate distances within
groups, distances between groups, and the net distance between groups.
Example 8.1:
From the main MEGA window, activate the data present in the "Crab_rRNA.meg" file.
If necessary, refer to Example 1.2 in the “MEGA Basics” tutorial.
From the main MEGA window launch bar, select Data | Select Taxa and Groups.
Notice the left pane called Taxa/Groups and the right pane labeled Ungrouped Taxa.
Press the New Group button found below the Taxa/Groups pane to add a new group to
the data. Name this new group “Pagurus” and press Enter.
While holding the Ctrl button on the keyboard, click on all of the items in the
Ungrouped Taxa pane that begin with Pagurus. This will highlight them. When they are
all highlighted, press the left-facing arrow button found on the vertical toolbar between
the two panels (make sure the Pagurus group on the left side is also highlighted
otherwise the arrow will not appear).
Select the All group in the Taxa/Groups panel and press the + (add) button found on the
vertical toolbar between the two window panes to add a second group. Name this group
"Non-Pagurus".
Add the remaining unassigned taxa to this group by using the left arrow and press the
Close button at the bottom of the window to exit this view.
Note: 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 option on the launch bar may be used to analyze the data.
Close all of the open windows.
41
Molecular Evolutionary Genetics Analysis
Computing Sequence Statistics
The “Drosophila_Adh.meg” data file, which is used in this tutorial, can be found in the
MEGA/Examples folder (The default location for Windows users is C:\Program
Files\MEGA\Examples. The default location for Mac users is $HOME/MEGA/Examples,
where $HOME is the user’s home directory).
Using Sequence Data Explorer
The Sequence Data Explorer provides various tools for visually analyzing sequence data as
well as calculating compositional statistics. In the following examples we will demonstrate the
basic usage of the Sequence Data Explorer.
Example 9.1:
Activate the "Drosophila_Adh.meg" file). If necessary, refer to Example 1.2 in the
“MEGA Basics” tutorial.
Select the Data | Explore Active Data (F4) command.
Use the arrow keys on your keyboard or the mouse to move from site to site. At the
bottom left corner of the window, you will find an indicator that displays the column
and the total number of sites. As you move through the columns, the column indicator
changes.
Highlighting
If you look at the bottom of the Sequence Data Explorer window, the Highlighted Sites
indicator displays "None" because no special site attributes are yet highlighted.
You can highlight variable sites in various ways:
•
•
•
Select the Highlight | Variable Sites main menu option on the Sequence Data Explorer
main screen.
Click the icon labeled V from the launch bar.
Press the V key on the keyboard.
Example 9.2:
Use one of the above methods to highlight variable sites in the Drosophila data. All sites
that are variable are now highlighted. The Highlighted indicator at the bottom of the
window has been replaced with the Variable indicator. The number of sites which are
variable is displayed, along with the total number of sites (Variable sites/Total # of
42
Part I: Getting Started
sites). When you press the V key again, the sites return to the normal color. The
Highlighted indicator again displays "None".
Now highlight the parsimony-informative sites by pressing the P key, clicking on the
button labeled Pi from the shortcut bar below the main menu, or selecting the Highlight
| Parsim-Info sites menu option. The Highlighted indicator turns into the Parsim-info
indicator.
To highlight 0, 2, and 4-fold degenerate sites, press the 0, 2, or 4 keys, respectively, or
click on the corresponding buttons from the shortcut bar below the main menu, or select
the corresponding command from the Highlight menu. Once again, the Highlighter
indicator will turn into the Zero-fold indicator, Two-fold indicator, and Four-fold
indicator respectively.
Statistics
The Statistics main menu option allows you to calculate Nucleotide Composition, Nucleotide
Pair Frequencies and Codon Usage. Before selecting one of these options, you will need to
select whether to use all sites or only the highlighted sites. You will also need to select the
format in which you want the results displayed.
Example 9.3:
Select Statistics | Use All Selected Sites. To display the results of the calculation in a
text file using the built-in text editor, click the Statistics menu option again and select
the Display Results in Text Editor option. To calculate the nucleotide base frequencies,
select the option, Nucleotide Composition, from the Statistics menu.
To compute codon usage, go back to the Sequence Data Explorer and 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.
To compute nucleotide pair frequencies, select the Statistics | Nucleotide Pair
Frequencies | Directional (16 pairs), or the Statistics | Nucleotide Pair Frequencies |
Undirectional (10 pairs) main menu option. This will calculate the pair frequencies and
display the results of the calculation in a text file using the built-in text editor.
Note: Notice that the Amino Acid Compositions option on the Statistics menu is disabled
(grayed-out). This option is only available if the sequences have been translated.
Using the Amino Acid Composition Option
43
Molecular Evolutionary Genetics Analysis
Example 9.4:
To translate these protein-coding sequences into amino acid sequences and back again,
select the Data | Translate Sequences main menu command from the Sequence Data
Explorer window.
Once the sequences are translated, calculate the amino acid composition by selecting the
Statistics | Amino Acid Composition main menu command from the Sequence Data
Explorer window.
Close the Text File Editor and Format Convertor window without saving your work.
Close the Sequence Data Explorer and select Close Data icon on the main MEGA
window.
44
Part I: Getting Started
Building Trees from Distance Data
This tutorial illustrates procedures for building phylogenetic trees using distance data.
The “Hum_Dist.meg” data file, which is used in this tutorial, can be found in the
MEGA/Examples folder (The default location for Windows users is C:\Program
Files\MEGA\Examples. The default location for Mac users is $HOME/MEGA/Examples,
where $HOME is the user’s home directory).
Making a Phylogenetic Tree from Distance Data
Example 10.1:
Activate the "Hum_Dist.meg" file. If necessary, refer to Example 1.2 in the “MEGA
Basics” tutorial.
From the main MEGA window, select Phylogeny | Construct/Test Neighbor-Joining
Tree from the launch bar.
The Analysis Preferences window will appear. For distance data files, all of the options
shown here cannot be changed. Click on the button labeled Compute. A progress meter
will appear briefly.
The Tree Explorer will display a neighbor-joining (NJ) tree on the screen when the
analysis completes.
From the Tree Explorer launch bar, click on the i icon. The number of tabs shown here
depends on the type of tree that was constructed. For a Neighbor-Joining tree, the tabs
are General, Tree and Branch. Take a look at each to see the information they contain.
Saving your Results
MEGA allows you to save trees in MEGA’s native format or in the Newick format.
Example 10.2:
From the Tree Explorer window, select File | Save Current Session. In the Save As
dialog, use the Save in drop-down menu to select the location, and then type in a name
for the session in the File Name area. The tree will be saved with the MEGA ".mts"
extension.
Now, from the Tree Explorer window, select File | Export Current Tree from the main
menu. In the Save As dialog, use the Save in drop-down to select the location. In the
45
Molecular Evolutionary Genetics Analysis
File Name area, type a name for the session. The tree will be saved in Newick format
with the ".nwk" extension.
Go to the File menu and click on the Exit Tree Explorer option.
46
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
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.
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Molecular Evolutionary Genetics Analysis
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 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 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).
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Part II: Assembling Data for Analysis
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 is 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.
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.
49
Molecular Evolutionary Genetics Analysis
Web Browser & Data Miner
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
the FASTA format by using the Display option on the web page shown.)
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.
Some Text Editor Utilities
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).
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Part II: Assembling Data for Analysis
Copy Screenshot to Clipboard
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.
51
Molecular Evolutionary Genetics Analysis
Format Selected Sequence
Utilities | Format Selected Sequence
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.
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Part II: Assembling Data for Analysis
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 does not validate whether the characters in the selected block are nucleotides.
53
Molecular Evolutionary Genetics Analysis
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 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 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.
Building Sequence Alignments
Alignment Explorer
The Alignment Explorer provides options to (1) view and manually edit alignments and (2)
generate alignments using a built-in CLUSTALW implementation and MUSCLE program (for
the complete sequence or data in any rectangular region). The Alignment Explorer also
prodes 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 (and
MUSCLE) alignment algorithms are initiated, they will only 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.
You may adjust the width of the sequence name column by clicking on the line which
separates the sequence names column and the start of the data column and dragging.
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Part II: Assembling Data for Analysis
Aligning Sequences
In this tutorial, we will show how to create a multiple sequence alignment from protein
sequence data that will be imported into the alignment editor using different methods. All of
the data files used in this tutorial can be found in the MEGA\Examples\ folder (The default
location for Windows users is C:\Program Files\MEGA\Examples\. The location for Mac
users is $HOME/MEGA/Examples, where $HOME is the user’s home directory).
Opening an Alignment
The Alignment Explorer is the tool for building and editing multiple sequence alignments in
MEGA.
Example 2.1:
Launch the Alignment Explorer by selecting the Align | Edit/Build Alignment on the
launch bar of the main MEGA window.
Select Create New Alignment and click Ok. A dialog will appear asking “Are you
building a DNA or Protein sequence alignment?” Click the button labeled “DNA”.
From the Alignment Explorer main menu, select Data | Open | Retrieve sequences
from File. Select the "hsp20.fas" file from the MEG/Examples directory.
Aligning Sequences by ClustalW
You can create a multiple sequence alignment in MEGA using either the ClustalW or Muscle
algorithms. Here we align a set of sequences using the ClustalW option.
Example 2.2:
Select the Edit | Select All menu command to select all sites for every sequence in the
data set.
Select Alignment | Align by ClustalW from the main menu to align the selected
sequences data using the ClustalW algorithm. Click the “Ok” button to accept the
default settings for ClustalW.
Once the alignment is complete, save the current alignment session by selecting Data |
Save Session from the main menu. Give the file an appropriate name, such as
"hsp20_Test.mas". This will allow the current alignment session to be restored for
future editing.
Exit the Alignment Explorer by selecting Data | Exit Aln Explorer from the main menu.
55
Molecular Evolutionary Genetics Analysis
Aligning Sequences Using Muscle
Here we describe how to create a multiple sequence alignment using the Muscle option.
Example 2.3:
Starting from the main MEGA window, select Align | Edit/Build Alignment from the
launch bar. Select Create a new alignment and then select DNA.
From the Alignment Explorer window, select Data | Open | Retrieve sequences from a
file and select the “Chloroplast_Martin.meg” file from the MEGA/Examples directory.
On the Alignment Explorer main menu, select Edit | Select All.
On the Alignment Explorer launch bar, you will find an icon that looks like a flexing
arm. Click on it and select Align DNA.
Near the bottom of the MUSCLE - AppLink window, you will see a row called
Alignment Info. You can scroll through the text to read information about the Muscle
program.
Click on the Compute button (accept the default settings). A Progress window will keep
you informed of Muscle alignment status. In this window, you can click on the
Command Line Output tab to see the command-line parameters which were passed to
the Muscle program. Note: The analysis may complete so fast, that you won’t be able
to click on this tab or read it. The information in this tab isn’t essential, it’s just
interesting.
When the Muscle program has finished, the aligned sequences will be passed back to
MEGA and displayed in the Alignment Explorer window.
Close the Alignment Explorer by selecting Data | Exit Aln Explorer. Select No when
asked if you would like to save the current alignment session to file.
Obtaining Sequence Data from the Internet (GenBank)
Using MEGA’s integrated browser you can fetch GenBank sequence data from the NCBI
website if you have an active internet connection.
Example 2.4:
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Part II: Assembling Data for Analysis
From the main MEGA window, select Align | Edit/Build Alignment from the main
menu.
When prompted, select Create New Alignment and click ok. Select DNA
Activate MEGA’s integrated browser by selecting Web | Query Genbank from the main
menu.
When the NCBI: Nucleotide site is loaded, enter CFS as a search term into the search
box at the top of the screen. Press the Search button.
When the search results are displayed, check the box next to any item(s) you wish to
import into MEGA.
If you have checked one box: Locate the dropdown menu labeled Display
Settings (located near the top left hand side of the page directly under the tab
headings). Change its value to FASTA and then click Apply. The page will
reload with all the search results in a FASTA format
If you have checked more than one box: locate the Display Settings dropdown
(located near the top left hand side of the page directly under the tab headings).
Change the value to FASTA (Text) and click the Apply button. This will output
all the sequences you selected as a text in the FASTA format.
Press the Add to Alignment button (with the red + sign) located above the web address
bar. This will import the sequences into the Alignment Explorer.
With the data now displayed in the Alignment Explorer, you can close the Web Browser
window.
Align the new data using the steps detailed in the previous examples.
Close the Alignment Explorer window by clicking Data | Exit Aln Explorer. Select No
when asked if you would like the save the current alignment session to file.
Note: We have aligned some sequences and they are now ready to be analyzed. Whenever you
need to edit/change your sequence data, you will need to open it in the Alignment Editor and
edit or align it there. Then export it to the MEGA format and open the resulting file.
57
Molecular Evolutionary Genetics Analysis
Aligning coding sequences via protein sequences
MEGA 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 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.
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)
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Part II: Assembling Data for Analysis
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 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).
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 closelyrelated 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 is 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.
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Molecular Evolutionary Genetics Analysis
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).
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.
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Part II: Assembling Data for Analysis
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.
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Molecular Evolutionary Genetics Analysis
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.
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.
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 displays the MUSCLE parameters dialog window, which is used
to configure MUSCLE 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.
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Part II: Assembling Data for Analysis
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.
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.
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Molecular Evolutionary Genetics Analysis
Site Number display on the status bar
Site #
64
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.
Part II: Assembling Data for Analysis
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.
Align by MUSCLE: 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 MUSCLE; to accept the parameters, press "OK". This initiates
the MUSCLE 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
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 unmark 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.
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Molecular Evolutionary Genetics Analysis
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.
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Part II: Assembling Data for Analysis
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 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 readonly.
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Molecular Evolutionary Genetics Analysis
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. There
are two formats to choose from: MEGA or FASTA formats. You will be requested to give a file
name to write the data to.
DNA Sequences: Use this item to specify that the input data is DNA. If DNA is 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.
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Part II: Assembling Data for Analysis
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 search 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 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.
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Molecular Evolutionary Genetics Analysis
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.
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Part II: Assembling Data for Analysis
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.
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Part III: Input Data Types and File Format
MEGA Input Data Formats
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, and 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.
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.
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Part III: Input Data Types and File Format
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].
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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.
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Part III: Input Data Types and File Format
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 data file, but MEGA 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.
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Molecular Evolutionary Genetics Analysis
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.
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Part III: Input Data Types and File Format
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;
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.
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Molecular Evolutionary Genetics Analysis
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
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.
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Part III: Input Data Types and File Format
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.
Symbols
Name
Remarks
A
Adenine
Purine
G
Guanine
Purine
C
Cytosine
Pyrimidine
T
Thymine
Pyrimidine
U
Uracil
Pyrimidine
R
Purine
A or G
Y
Pyrimindine
C or T/U
DNA/RNA
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
Phenylalanine
Phe
G
Glycine
Gly
Protein
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Molecular Evolutionary Genetics Analysis
H
Histidine
His
I
Isoleucine
Ile
K
Lysine
Lys
L
Leucine
Leu
M
Methionine
Met
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
*
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Part III: Input Data Types and File Format
Keywords for Format Statement (Sequence data)
Command
Setting
Remark
Example
DataType
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
Property
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 this point.
Property=cyt_b
Coding,
Noncoding,
and End.
Indel
single
character
Use dash (-) to
identify
insertion/deletions
in sequence
alignments
Indel = -
Identical
single
character
Use period (.) to
show identify with
the first sequence.
Identical = .
MatchChar
single
character
Synonymous with
the identical
keyword.
MatchChar = .
Missing
single
character
Use a question
mark (?) to
indicate missing
data.
Missing = ?
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Molecular Evolutionary Genetics Analysis
CodeTable
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
Domain=Intron Property=Noncoding;
#Human
ATTCCCAGGGAATTCCCGGGGGGTTTAAGGCCCCTTTAAAGAAAGAT
#Mouse
GTAGCGCGCGTCGTCAGAGCTCCCAAGGGTAGCAGTCACAGAAAGAT
#Chicken
GTAAAAAAAAAAGTCAGAGCTCCCCCCAATATATATCACAGAAAGAT
!Gene=ThirdGene
Domain=Exon2
Property=Coding;
#Human
ATCTGCTCTCGAGTACTGATACAAATGACTTCTGCGTACAACTGA
#Mouse
ATCTGATCTCGTGTGCTGGTACGAATGATTTCTGCGTTCAACTGA
#Chicken
ATCTGCTCTCGAGTACTGCTACCAATGACTTCTGCGTACAACTGA
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Part III: Input Data Types and File Format
Keywords for Command Statements (Genes/Domains)
Command
Setting
Remark
Example
Domain
A name
This instruction defines a
domain with the given
name
Domain=first_ex
on
Gene
A name
This instruction defines a
gene with the given
name
Gene=cytb
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.
Property=cytb
This instruction specifies
the site where the next
1st-codon position will be
found in a protein-coding
domain.
CodonStart=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
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.
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Molecular Evolutionary Genetics Analysis
!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
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Part III: Input Data Types and File Format
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
Site Label
The individual sites in nucleotide or amino acid data can be labeled to construct noncontiguous 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 Third Gene are labeled with a ‘+’ mark.
An underscore marks an absence of any labels.
!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.
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Molecular Evolutionary Genetics Analysis
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.
Labeled sites work independently of and in addition to genes and domains, thus allowing
complex subsets of sites to be defined easily.
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
4.0
3.0
2.5
4.6
1.3
3.6
4.2
In the above example, pairwise distances are written in the upper triangular matrix (upper-right
format). Two alternate distance matrix formats are:
Lower-left matrix
Upper-right matrix
d12
d12
d13
d23
d14
d24
d34
d15
d25
d35
86
d45
d13
d14
d15
d23
d24
d25
d34
d35
d45
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
DataFormat
Lowerleft,
upperright
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
d12
d12
d13
d23
d14
d24
d34
d15
d25
d35
d13
d14
d15
d23
d24
d25
d34
d35
d45
d45
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
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.
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Molecular Evolutionary Genetics Analysis
!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
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Part III: Input Data Types and File Format
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.
Tree Input Data
Tree Data
* This section of the online help will be available in future updates of MEGA.
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Molecular Evolutionary Genetics Analysis
Display Newick Trees from File
Explorer/Editor | 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).
Importing Data from other Formats
Importing Data From Other Formats
MEGA supports conversions from several different file formats into MEGA formats. Each
format is indicated by the file extension used. Supported formats include:
Extension
File type
. an
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 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.
90
Part III: Input Data Types and File Format
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.
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Molecular Evolutionary Genetics Analysis
Convert To MEGA Format (Main File Menu)
Data | Activate Data for Analysis | 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
5 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.
Format Specific Notes
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 into the following format:
#mega
Title: Bigrab2.aln
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Part III: Input Data Types and File Format
#Q9Y2J0_Hsa
------------MTDTVFSNSSNRWMYPSDRPLQSNDKEQLQAGWSVHPG
GQPDRQRKQEELTDEEKEIINRVIARAEKMEEMEQER--IGRLVDRLENM
RKNVAGDGVNRCILCGEQLGMLGSACVVCEDCKKNVCTKCGVET-NNRLH
#Q06846_RP3A_BOVIN
------------MTDTVFSSSSSRWMCPSDRPLQSNDKEQLQTGWSVHPS
GQPDRQRKQEELTDEEKEIINRVIARAEKMEEMEQER--IGRLVDRLENM
RKNVAGDGVNRCILCGEQLGMLGSACVVCEDCKKNVCTKCGVETSNNRPH
#JX0338_rabphilin-3A-mouse
------------MTDTVVN----RWMYPGDGPLQSNDKEQLQAGWSVHPG
AQTDRQRKQEELTDEEKEIINRVIARAEKMEAMEQER--IGRLVDRLETM
RKNVAGDGVNRCILCGEQLGMLGSACVVCEDCKKNVCTKCGVETSNNRPH
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Molecular Evolutionary Genetics Analysis
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 a
‘>’) as the sequence data. The MEGA file above would convert as follows:
#mega
Title: infile.fasta
#G019uabh
ATACATCATAACACTACTTCCTACCCATAAGCTCCTTTTAACTTGTTAAAGTCTTGCTTG
AATTAAAGACTTGTTTAAACACAAAAATTTAGAGTTTTACTCAACAAAAGTGATTGATTG
ATTGATTGATTGATTGATGGTTTACAGTAGGACTTCATTCTAGTCATTATAGCTGCTGGC
AGTATAACTGGCCAGCCTTTAATACATTGCTGCTTAGAGTCAAAGCATGTACTTAGAGTT
94
Part III: Input Data Types and File Format
GGTATGATTTATCTTTTTGGTCTTCTATAGCCTCCTTCCCCATCCCCATCAGTCTTAATC
AGTCTTGTTACGTTATGACTAATCTTTGGGGATTGTGCAGAATGTTATTTTAGATAAGCA
AAACGAGCAAAATGGGGAGTTACTTATATTTCTTTAAAGC
#G028uaah
CATAAGCTCCTTTTAACTTGTTAAAGTCTTGCTTGAATTAAAGACTTGTTTAAACACAAA
ATTTAGACTTTTACTCAACAAAAGTGATTGATTGATTGATTGATTGATTGATGGTTTACA
GTAGGACTTCATTCTAGTCATTATAGCTGCTGGCAGTATAACTGGCCAGCCTTTAATACA
TTGCTGCTTAGAGTCAAAGCATGTACTTAGAGTTGGTATGATTTATCTTTTTGGTCTTCT
ATAGCCTCCTTCCCCATCCCATCAGTCT
95
Molecular Evolutionary Genetics Analysis
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
..
0
Length: 428
Mon Sep 25 17:34:20 MDT 2000
Check:
1 MSKEHVQTIA TDDVSKNGHT PPTNASTPPY PFVAIVGQAE LKLALLLCVV
51 NPTIGGVMVM GHRGTAKSTA VRALAAMLPP IKAVAGCPYS CAPDRTAGLC
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
96
Part III: Input Data Types and File Format
KLEVDGHRGE LTLARA.ATA LAALEGRNEV TVQDVRRIAV LALRHRLRKD
PLETQD.... ...DAVRIER AVEEVLVP.. .......... ..........
.......... .......... ........
97
Molecular Evolutionary Genetics Analysis
Converting IG Format Files
IG Format
These files consist of one or more groups of non-blank lines separated by one or more 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. 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 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 non-sequence data, such as
comments, headers, etc.
The example converts to MEGA file format as follows:
#mega
!Title: filename
#G019uabh
ATACATCATAACACTACTTCCTACCCATAAGCTCCTTTTAACTTGTTAAA
GTCTTGCTTGAATTAAAGACTTGTTTAAACACAAAAATTTAGAGTTTTAC
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Part III: Input Data Types and File Format
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
0 ..
MSF: 240
Type: N
Wed Sep 20 12:57:06 MDT 2000
Name: G019uabh
Len: 400 Check: 0 Weight: 1.00
Name: G028uaah
Len:
268 Check:
Name: G022uabh
Len:
257 Check:
Name: G023uabh
Len:
347 Check:
Name: G006uaah
Len:
240 Check:
//
G019uabh
ACTTGTTAAA
G028uaah
AAGACTTGTT
G022uabh
ATCAATCCTG
G023uabh
CATAAAATAA
G006uaah
ATATGCTTTG
G019uabh
AGAGTTTTAC
G028uaah
ATTGATTGAT
G022uabh
TTCACTATCC
G023uabh
CTTATGTTTA
G006uaah
TCAGCTTAAT
G019uabh
TTTACAGTAG
G028uaah
TTATAGCTGC
G022uabh
CTACCCATAA
G023uabh
AATCCTCTAC
G006uaah
GTAGAAAATG
G019uabh
GCCAGCCTTT
G028uaah
GAGTCAAAGC
0
0
0
0
Weight:
Weight:
Weight:
Weight:
Check:
1.00
1.00
1.00
1.00
ATACATCATA ACACTACTTC CTACCCATAA GCTCCTTTTA
CATAAGCTCC TTTTAACTTG TTAAAGTCTT GCTTGAATTA
TATTTTAGAG ACCCAAGTTT TTGACCTTTT CCATGTTTAC
AATAAATACC AAAAAAATAG TATATCTACA TAGAATTTCA
ACATAAAATA AACTGTTTTC TATGTGAAAA TTAACCTANN
GTCTTGCTTG AATTAAAGAC TTGTTTAAAC ACAAAAATTT
TAAACACAAA ATTTAGACTT TTACTCAACA AAAGTGATTG
TAGGTGATTG GGCAGCCATT TAAGTATTAT TATAGACATT
ACTGTTTTCT ATGTGAAAAT TAACCTAAAA ATATGCTTTG
CTTATGTTTA AGATGTCATG CTTTTTATCA GTTGAGGAGT
TCAACAAAAG TGATTGATTG ATTGATTGAT TGATTGATGG
TGATTGATTG ATGGTTTACA GTAGGACTTC ATTCTAGTCA
CATTAAAACC CTTTATGCCC ATACATCATA ACACTACTTC
AGATGTCATG CTTTTTATCA GTTGAGGAGT TCAGCTTAAT
AATCCTCTAA GATCTTAAAC AAATAGGAAA AAAACTAAAA
GACTTCATTC TAGTCATTAT AGCTGCTGGC AGTATAACTG
TGGCAGTATA ACTGGCCAGC CTTTAATACA TTGCTGCTTA
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Molecular Evolutionary Genetics Analysis
G022uabh
TTGTTTAAAC
G023uabh
GAAATAAAAT
G006uaah
CTTATAACAT
G019uabh
GGTATGATTT
G028uaah
ATAGCCTCCT
G022uabh
TTGATTGATT
G023uabh
GAAATGCTTT
G006uaah
G019uabh
AGTCTTAATC
G028uaah
G022uabh
G023uabh
GTTTTTGCTT
G019uabh
AATGTTATTT
G023uabh
ACGTTAC
G019uabh
TCTTTAAAGC
GCTCCTTTTA ACTTGTTAAA GTCTTGCTTG AATTAAAGAC
GATCTTAAAC AAATAGGAAA AAAACTAAAA GTAGAAAATG
GAAATAAAAT GTCAAAGCAT TTCTACCACT CAGAATTGAT
AATACATTGC TGCTTAGAGT CAAAGCATGT ACTTAGAGTT
ATGTACTTAG AGTTGGTATG ATTTATCTTT TTGGTCTTCT
ACAAAATTTA GACTTTTACT CAACAAAAGT GATTGATTGA
GTCAAAGCAT TTCTACCACT CAGAATTGAT CTTATAACAT
GAAATGCTTT TTAAAAGAAA ATATTAAAGT TAAACTCCCC
ATCTTTTTGG TCTTCTATAG CCTCCTTCCC CATCCCCATC
TCCCCATCCC ATCAGTCT
GATTGAT
TTAAAAGAAA ATATTAAAGT TAAACTCCCC TATTTTGCTC
AGTCTTGTTA CGTTATGACT AATCTTTGGG GATTGTGCAG
ATCTAAAATA CATTCTGCAC AATCCCCAAA GATTGATCAT
TAGATAAGCA AAACGAGCAA AATGGGGAGT TACTTATATT
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
#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
100
Part III: Input Data Types and File Format
#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
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
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Molecular Evolutionary Genetics Analysis
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-- ---------- ------------------- ---------- --------*
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-- ---------- ------------------- ---------- --------
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Part III: Input Data Types and File Format
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Molecular Evolutionary Genetics Analysis
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]
MATRIX
Chloroflex MSKEHVQTIATDDVSKNGHT PPTNASTPPYPFVAIVGQAE
Rcapsulatu ---------MTTAVARLQPS ASGAKTRPVFPFSAIVGQED
Chloroflex DQCRALEQQSGKTKKPAVIN IPVPVVDLPLGATEDRVCGT
Rcapsulatu DWATVLS-----TN---VIR KPTPVVDLPLGVSEDRVVGA
The MEGA 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
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Part III: Input Data Types and File Format
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Molecular Evolutionary Genetics Analysis
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, 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:
2 2000 I
G019uabh
ATACATCATA ACACTACTTC CTACCCATAA GCTCCTTTTA ACTTGTTAAA
G028uaah
CATAAGCTCC TTTTAACTTG TTAAAGTCTT GCTTGAATTA AAGACTTGTT
GTCTTGCTTG AATTAAAGAC TTGTTTAAAC ACAAAAATTT AGAGTTTTAC
TAAACACAAA ATTTAGACTT TTACTCAACA AAAGTGATTG ATTGATTGAT
TCAACAAAAG TGATTGATTG ATTGATTGAT TGATTGATGG TTTACAGTAG
TGATTGATTG ATGGTTTACA GTAGGACTTC ATTCTAGTCA TTATAGCTGC
MEGA 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
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Part III: Input Data Types and File Format
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
ATACATCATA ACACTACTTC CTACCCATAA GCTCCTTTTA ACTTGTTAAA
GTCTTGCTTG AATTAAAGAC TTGTTTAAAC ACAAAAATTT AGAGTTTTAC
TCAACAAAAG TGATTGATTG ATTGATTGAT TGATTGATGG TTTACAGTAG
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
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
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
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Molecular Evolutionary Genetics Analysis
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Part III: Input Data Types and File Format
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
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
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Molecular Evolutionary Genetics Analysis
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Part III: Input Data Types and File Format
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. 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>
<mol>DNA</mol>
<cksum>302C447C</cksum>
<seqdata>ATACATCATAACACTACTTCCTACCCATAAGCTCCTTTTAACTTGTTAAAGTCTTGCTTGAATT
AAAGACTTGTTTAAACACAAAAATTTAGAGTTTTACTCAACAAAAGTGATTGATTGATTGATTGATTGA
TTGATGGTT
TACAGTAGGACTTCATTCTAGTCATTATAGCTGCTGGCAGTATAACTGGCCAGCCTTTAATACATTGCT
GCTTAGAGT
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
111
Molecular Evolutionary Genetics Analysis
#G019uabh
ATACATCATAACACTACTTCCTACCCATAAGCTCCTTTTAACTTGTTAAAGTCTTGCTTGAATT
AAAGACTTGTTTAAACACAAAAATTTAGAGTTTTACTCAACAAAAGTGATTGATTGATTGATTGATTGA
TTGATGGTT
TACAGTAGGACTTCATTCTAGTCATTATAGCTGCTGGCAGTATAACTGGCCAGCCTTTAATACATTGCT
GCTTAGAGT
Genetic Code Tables
Built-in Genetic Codes
MEGA contains four commonly used genetic code tables: (1) Standard, (2) Vertebrate
mitochondrial, (3) Drosophila mitochondrial, and (4) Yeast mitochondrial as well as 19 others.
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
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Part III: Input Data Types and File Format
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
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
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Molecular Evolutionary Genetics Analysis
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.
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Part III: Input Data Types and File Format
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 this information can be computed for any code table in
the Genetic Code Selector. In addition to the degeneracy of the codon positions, MEGA 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
No. of
Sites for
Redundancy
Codon
codon
Po
Po s
s 2n Pos
S
N
1st d
3rd
UUU
(F)
0.33
3
2.66
7
0
0
2
UUC
(F)
0.33
3
2.66
7
0
0
2
UUA
(L)
0.66
7
2.33
3
2
0
2
UUG
(L)
0.66
7
2.33
3
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
1
2
0
0
2
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Molecular Evolutionary Genetics Analysis
(Y)
116
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)
1.33
3
1.66
7
2
0
4
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.
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Molecular Evolutionary Genetics Analysis
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.
Viewing and Exploring Input Data
Sequence Data Explorer
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.
This explorer consists of a number of regions as follows:
Menu Bar
Data menuHC_Data_Menu_in_sequence_Data_Explorer
Search menu
Display menuHC_Display_Menu_in_Sequence_Data_Explorer
Highlight menuHC_Highlight_Menu_in_Sequence_Data_Explorer
Statistics menuHC_Stats_Menu_in_Sequence_Data_Explorer
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
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Part III: Input Data Types and File Format
: This brings up the Exporting Sequence Data dialog box, which contains
options to control how MEGA writes the output data, available options are Text, MEGA, CSV,
and Excel.
: This brings up the Exporting Sequence Data dialog box and sets the default output format
to MEGA.
: This brings up the Exporting Sequence Data dialog box and sets the default output format
to Excel.
: This brings up the Exporting Sequence Data dialog box and sets the default output format
to CSV (Comma separated values).
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 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).
Special: This dropdown allows for the selection of a special highlighting option.
CpG/TpG/CpA: if this button is pressed, then all sites which have a C followed by a G, T by G,
or C by A will be highlighted. You may also select a percentage of sequences which must
have these properties for a site to be counted.
Coverage: if this button is pressed, then you will enter a percentage. All the sites with this
percentage or less of ambiguous sites will be highlighted.
119
Molecular Evolutionary Genetics Analysis
: 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.
Searching
: This button allows you to specify a sequence name to find. Search results are bolded and
the row is highlighted blue. MEGA first looks for an exact match to the name you specified, if
none exists it looks for names starting with what you provided, if no names start with the
provided search term, then MEGA looks for your search term anywhere in the names(rather
than just the start).
: This button allows you to specify a Motif to search for in the sequence data. This Motif
supports IUPAC codes such as R (for A or G) and Y (for T or C). MEGA highlights (in Yellow)
the first instance of this motif it finds.
and
: These buttons are only enabled if you have already searched for a Sequence
Name or Motif. By clicking the forward or backward button MEGA will search for the next or
previous search result (assuming there is more than one possible matches).
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
This allows you to explore the active data set, and establish various data attributes, and data
subset options. It also 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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Part III: Input Data Types and File Format
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Molecular Evolutionary Genetics Analysis
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
FileHC_Export_Data
_in_Sequence_Data
_Explorer
Brings up the Exporting
Sequence Data dialog box.
Translate/Untranslat
eHC_Translate_Untr
anslate_in_Sequenc
e_Data_Explorer
Translates protein-coding
nucleotide sequences into
protein sequences, and
back to nucleotide
sequences.
Select Genetic Code
TableHC_Select_Ge
netic_Code_Table_in
_Sequence_Data_Ex
plorer
Brings up the Select
Genetic Code dialog box, in
which you can select, edit or
add a genetic code table.
Setup/Select Genes
and
DomainsHC_Setup_
Select_Genes_Dom
ains_in_Sequence_
Data_Explorer_
Brings up the Sequence
Data Organizer, in which
you can define and edit
genes and domains.
Setup/Select Taxa
and
GroupsHC_Setup_S
elect_Taxa_Groups_
in_Sequence_Data_
Explorer
Brings up the Select/Edit
Taxa and Groups dialog, in
which you can edit taxa and
define groups of taxa.
Quit Data
ViewerHC_Quit_Dat
a_Viewer
Takes the user back to the
main interface.
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Part III: Input Data Types and File Format
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.
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Molecular Evolutionary Genetics Analysis
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 Menu.
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Part III: Input Data Types and File Format
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.
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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.
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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 non-coding 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.
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Quit Data Viewer
Data | Quit Data Viewer
This command closes the Sequence Data Explorer, and takes the user back to main interface.
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.
Select Color: This option changes the color for highlighted sites. It is Yellow by default.
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.
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Restore Input Order
Display | Restore Input Order
Choosing this restores the order in Sequence Data Explorer to that in the input text file.
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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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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:
Symbo
l
Color
A
Yellow
G
Fuchsi
a
C
Olive
T
Green
U
Green
For amino acid sequences:
Symbo
l
Color
Symbol
Color
A
Yello
w
M
Yellow
C
Olive
N
Green
D
Aqua
P
Blue
E
Aqua
Q
Green
F
Yello
w
R
Red
G
Fuchs
ia
S
Green
H
Teal
T
Green
I
Yello
w
V
Yellow
K
Red
W
Green
L
Yello
w
Y
Lime
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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.
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Show Sequence Names
Display | Show Sequence Names
This option displays the full sequence names in Sequence Data Explorer
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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
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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
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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.
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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.
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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.
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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 dragging-and-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.
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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.
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Highlight Conserved Sites
Highlight | Conserved Sites
Use this command to highlight constant sites
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Highlight Variable Sites
Highlight | Variable Sites
Use this command to highlight variable sites sites.
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Highlight Singleton Sites
Highlight | Singleton Sites
Use this command to highlight singleton sites.
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Highlight Parsimony Informative Sites
Highlight | Parsim-Info Sites
Use this command to highlight parsimony-informative sites.
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Highlight 0-fold Degenerate Sites
Highlight | 0-fold Degenerate Sites
Use this command to highlight 0-fold degenerate sites.
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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.
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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:
Nucleotide CompositionHC_Nucleotide_Composition.
Nucleotide Pair FrequenciesHC_Nucleotide_Pair_Frequencies.
Codon UsageHC_Codon_Usage.
Amino Acid CompositionHC_Amino_Acid_Composition.
Use All Selected SitesHC_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.
Display results in Excel (XL) - Only effects outputs from the Statistics menu
Display results in Comma-Delimited (CSV) - Only effects outputs from the Statistics menu
Display results in Text Editor - Only effects outputs from the Statistics menu
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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).
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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 by
domain (if domains have been defined in Setup/Select Genes & Domains).
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Codon Usage
Statistics | Codon Usage
This command is visible only if the data contains protein-coding nucleotide sequences. MEGA
computes the percent codon usage and the RCSU values for each codon for all sequences
included in the dataset. Results will be displayed in by domain (if domains have been defined
in Setup/Select Genes & Domains).
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Amino Acid Composition
Statistics | Amino acid Composition
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 by
domain (if domains have been defined in Setup/Select Genes & Domains).
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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.
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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.
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 menuHC_Dist_Exp_File_Menu
Display menuHC_Dist_Exp_Display_Menu
Average menuHC_Dist_Exp_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 which contains options to control how
MEGA writes the output data, available options are Text, MEGA, CSV, and Excel.
: 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 rowheaders; the column headers are numbered from 1 to m, m being the number of sequences.
There is a column sizer button for the row-headers, so you can increase or decrease the
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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.
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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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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.
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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 within-group 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 averages, 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".
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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, and the maximum number of distances per line in the matrix.
When exporting to Excel or CSV you can choose to export as either a normal matrix or in a
column format (Species 1, Species 2, Distance, Std Err.). The standard Matrix has a limit of
255 columns (that means 255 taxa) due to a limit imposed by Excel caused by a maximum
number of columns.
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.
Text Editor
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
File menu
The File Menu contains the functions that
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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 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 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
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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).
Using Text Editor
File Menu
New (in Text Editor)
File | New
Use this command to create a new file in the Text Editor.
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Open (in Text Editor)
File | Open
Use this command to open an existing file in the Text Editor.
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Reopen (in Text Editor)
File | Reopen
Choose this command to reopen a recently closed text file from the most-recently-used-files list. When you close a
text file in the Text Editor, it is added to the Reopen list.
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Select All (in Text Editor)
Edit | Select All
This is used to select (highlight) everything in the displayed file.
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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 to which you want to move.
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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.
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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.
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Save (in Text Editor)
File | Save
This allows you to save the file currently being edited.
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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
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Print (in Text Editor)
File | Print
This command will print the currently displayed file to the selected printer.
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Close File (in Text Editor)
File | Close File
This closes the current file.
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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.
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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.
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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.
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Paste (in Text Editor)
Edit | Paste
This inserts the most recently copied text present on the Windows clipboard.
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Undo (in Text Editor)
Edit | Undo
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.
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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.
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Find Again (in Text Editor)
Search | Find Again
Choose this to repeat the last Find command.
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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.
Visual Tools for Data Management
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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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 multi-gene family may be arranged into groups consisting of orthologous
sequences.
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:
Select Taxa (sequences) or Groups of taxa through the Setup/Select Taxa & Groups dialog
box,
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.
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.
Choose to include only the Labeled Sites through the Data | Select Preferences menu item.
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.
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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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Computing Basic Statistical Quantities for Sequence Data
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.
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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.
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Molecular Evolutionary Genetics Analysis
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 by
domain (if domains have been defined in Setup/Select Genes & Domains).
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Part IV: Evolutionary Analysis
Codon Usage
Statistics | Codon Usage
This command is visible only if the data contains protein-coding nucleotide sequences. MEGA
computes the percent codon usage and the RCSU values for each codon for all sequences
included in the dataset. Results will be displayed in by domain (if domains have been defined
in Setup/Select Genes & Domains).
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Molecular Evolutionary Genetics Analysis
Pattern tests
The substitution pattern homogeneity between sequences (Kumar and Gadagkar 2001)
Compute Pattern Disparity Index (disparity index) and Compute Composition Distances
(pairwise sequence composition distance) are two test statistics related to the substitution
pattern homogeneity test. (Kumar and Gadagkar 2001).
Computing Evolutionary Distances
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, Synnonsynonymous, and Amino acid):
Nucleotide
Sequences are compared nucleotide-by-nucleotide. These distances can be computed
for protein coding and non-coding nucleotide sequences.
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
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Part IV: Evolutionary Analysis
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
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
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Molecular Evolutionary Genetics Analysis
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.
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.
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Part IV: Evolutionary Analysis
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.
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.
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Molecular Evolutionary Genetics Analysis
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
where p is the proportion of sites with different nucleotides.
Variance
See also Nei and Kumar (2000), page 36.
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Part IV: Evolutionary Analysis
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.
Variance
See also Nei and Kumar (2000), page 38.
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Molecular Evolutionary Genetics Analysis
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
respectively, and
Variances
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Part IV: Evolutionary Analysis
where
See also Nei and Kumar (2000), page 37.
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Molecular Evolutionary Genetics Analysis
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.
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
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Part IV: Evolutionary Analysis
where
See also Nei and Kumar (2000), page 39.
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Molecular Evolutionary Genetics 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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Part IV: Evolutionary Analysis
Variances
where
See also Nei and Kumar (2000), page 40.
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Molecular Evolutionary Genetics Analysis
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 distance, 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).
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).
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Part IV: Evolutionary Analysis
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
Number of sites compared.
common sites
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
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Molecular Evolutionary Genetics Analysis
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 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.
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Part IV: Evolutionary Analysis
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
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
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Molecular Evolutionary Genetics Analysis
where
See also Nei and Kumar (2000), page 44 and estimating gamma parameter.
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Part IV: Evolutionary Analysis
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
where xij is the relative frequency of the nucleotide pair i and j, gi’s are the nucleotide
frequencies.
Variance
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Molecular Evolutionary Genetics Analysis
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
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
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Part IV: Evolutionary Analysis
Variances
where
See also Nei and Kumar (2000), page 45 and estimating gamma parameter.
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Molecular Evolutionary Genetics Analysis
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 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
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Part IV: Evolutionary Analysis
Variances
where
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Molecular Evolutionary Genetics Analysis
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
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.
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Part IV: Evolutionary Analysis
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.
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
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Molecular Evolutionary Genetics Analysis
The variances can be estimated by the bootstrap method. .
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Part IV: Evolutionary Analysis
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
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
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Molecular Evolutionary Genetics Analysis
The variances can be estimated by the bootstrap method.
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Part IV: Evolutionary 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, and 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
Number of sites compared.
common sites
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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Molecular Evolutionary Genetics 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.
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Part IV: Evolutionary Analysis
Tamura-Nei distance (Gamma rates and Heterogeneous patterns)
The Tamura-Nei (1993) distance 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 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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Molecular Evolutionary Genetics Analysis
The variances can be estimated by the bootstrap method.
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Part IV: Evolutionary Analysis
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.
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
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Molecular Evolutionary Genetics Analysis
The variances can be estimated by the bootstrap method.
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Part IV: Evolutionary Analysis
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, and 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:
Quantity
Description
d: distance
Number of sites
different.
L: No of valid
Number of sites
common sites
compared.
The formulas used are:
Quantity
Formula
Variance
None
See also Nei and Kumar (2000), page 18.
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Molecular Evolutionary Genetics Analysis
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
Number of sites compared.
common sites
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.
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Part IV: Evolutionary Analysis
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
lineages.
MEGA provides facilities to compute the following quantities:
Quantity
Description
d: distance
Number of amino acid
substitutions per site.
L: No of valid
Number of sites compared.
common sites
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
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Molecular Evolutionary Genetics Analysis
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
Number of sites compared.
common sites
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 maximumlikelihood 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
Number of sites compared.
common sites
The variance of d can be estimated by the bootstrap method.
Gamma Distances
Computing the Gamma Parameter (a)
In the computation of gamma distances, it is necessary to know the gamma parameter (a).
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).
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Molecular Evolutionary Genetics Analysis
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 maximumlikelihood 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
Number of sites compared.
common sites
The variance of d can be estimated by the bootstrap method.
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Part IV: Evolutionary Analysis
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
Number of sites compared.
common sites
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 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
Number of sites compared.
common sites
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
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Molecular Evolutionary Genetics Analysis
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
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
226
Formula
Variance
Part IV: Evolutionary Analysis
Dp
Dd
See also Nei and Kumar (2000), page 52
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Molecular Evolutionary Genetics Analysis
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 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
D
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Variance
Part IV: Evolutionary Analysis
See also Nei and Kumar (2000), page 52.
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Molecular Evolutionary Genetics Analysis
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
ity
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Formula
Variance
Part IV: Evolutionary Analysis
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.
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Molecular Evolutionary Genetics Analysis
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
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
d4
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Formula
Variance
Part IV: Evolutionary Analysis
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.
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Molecular Evolutionary Genetics Analysis
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
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 Pamilo-Bianchi-Li
methods is arbitrary, which sometimes creates a problem because the arginine codons occur
quite frequently. Comeron (1995) addressed this problem by dividing the 2-fold 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
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Part IV: Evolutionary Analysis
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-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 0fold, 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
v0
V2
v2S
v2N
v4
(v)
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 i-fold 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.
See also Nei and Kumar (2000), page 64.
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Molecular Evolutionary Genetics Analysis
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 yellow 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
Variance Estimation Method
Use this to specify whether to compute Distances only or Distances and Standard
Errors using the selected estimation method. If you select the latter, then you are given
a choice as to how to compute it in the No. of Bootstrap Replications box.
When you compute average distance or diversity, only the bootstrap method is
available for computing standard errors.
Substitution Model
In this set of options, you choose the various attributes of the substitution models.
Substitutions Type
Here you may select a substitutions type of Nucleotide, Syn-Nonsynonymous, or
Amino Acid. The selection in this row effects the available models in the model row.
Model
Here you select a stochastic model for estimating evolutionary distance by clicking on
the row then selecting a model for the current Substitutions Type.
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 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.
Data Subset to Use
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
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Part IV: Evolutionary Analysis
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), or you may use Partial Deletion (Site
coverage) as a percentage.
Codon Positions
Check or uncheck the boxes for 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 row, 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.
237
Molecular Evolutionary Genetics Analysis
Distance Model Options
With this option, you can choose the general attributes of the substitution models for DNA and
protein sequence evolution.
Substitutions Type
Here you may select a substitutions type of Nucleotide, Syn-Nonsynonymous, or Amino Acid.
The selection in this row effects the available models in the model row.
Model
You can select a stochastic model for estimating evolutionary distances by clicking on the row
then selecting a model for the current Substitutions Type.
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 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.
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Part IV: Evolutionary Analysis
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 pseudorandom number generator. In each
bootstrap replicate, the desired quantity is estimated and the standard deviation of the
original values is 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).
Constructing Phylogenetic Trees
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.
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).
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 yellow 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 the Bootstrap test. This
test uses the bootstrap re-sampling strategy, so you need to enter the number of
replicates. For a given data set applicable tests and the phylogeny inference method
are enabled. Neighbor joining has an additional test Interior Branch which requires the
same input as bootstrap.
Substitution Model
In this set of options, you can choose various attributes of the substitution models for
DNA and protein sequences.
Substitutions Type
Here you may select a substitutions type of Nucleotide, Syn-Nonsynonymous, or
239
Molecular Evolutionary Genetics Analysis
Amino Acid. The selection in this row effects the available models in the model row.
Model
Here you select a stochastic model for estimating evolutionary distance by clicking on
the row then selecting a model for the current Substitutions Type.
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 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.
Data Subset to Use
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 (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), or you may use Partial Deletion (Site
coverage) as a percentage.
Codon Positions
Check or uncheck the boxes for 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 row, 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.
Minimum Evolution Method
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Part IV: Evolutionary Analysis
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 timeconsuming 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 neighborjoining 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 topology 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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Molecular Evolutionary Genetics 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 yellow row indicates that you have a choice for that
particular attribute. The primary sets of options available in this dialog box are:
Analysis
Test of Phylogeny
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. For a given data set applicable tests and the phylogeny inference
method are enabled.
Substitution Model
In this set of options, you can choose various attributes of the substitution models for
DNA and protein sequences.
Substitutions Type
Here you may select a substitutions type of Nucleotide, Syn-Nonsynonymous, or
Amino Acid. The selection in this row effects the available models in the model row.
Model
Here you select a stochastic model for estimating evolutionary distance by clicking on
the row then selecting a model for the current Substitutions Type.
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 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.
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 (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), or you may use Partial Deletion (Site
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coverage) as a percentage.
Codon Positions
Check or uncheck the boxes for 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 row, 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.
Tree Inference Options
ME Heuristic Method
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.
Initial Tree For ME
This is obtained by using Neighbor Joining.
ME Search Level
Select a search level 1 or 2.
Maximum Parsimony (MP) Method
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 branchand-bound search, which is described in detail in Kumar et al. (1993) and Nei and Kumar
(2000, page 123).
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Molecular Evolutionary Genetics Analysis
Alignment Gaps and Sites with Missing Information
In MEGA, gap sites are ignored in the MP analysis, but there are three 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. The final option is Partial Deletion
which deletes the gaps assuming there are less than a certain percentage of gaps
(unambiguous).
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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).
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Molecular Evolutionary Genetics Analysis
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 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 missing-information
before the parsimony analysis begins using the Complete-deletion option.
Alternatively, you may choose to retain all such sites. In this case, all missinginformation and alignment gap sites are treated as missing data in the calculation of
tree length. Your last option is partial deletion a.k.a. coverage where you may select a
percentage where only sites above that percentage of unambiguity will be counted.
Codon Positions
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.
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.
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Heuristic Search
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)
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Molecular Evolutionary Genetics Analysis
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.
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).
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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.
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 | Construct/Test Neighbor-Joining Tree
Or
Phylogeny | Construct/Test Minimum-Evolution Tree
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. Select test of phylogeny for either of these trees in the Analysis
Preferences dialog.
See Nei and Kumar (2000) (chapter 9) for further details.
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Molecular Evolutionary Genetics Analysis
Neighbor Joining (Construct Phylogeny)
Phylogeny | Construct/Test Neighbor-Joining Tree
This command is used to construct (or Test) 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 | Construct/Test Neighbor-Joining Tree
Or
Phylogeny | Construct/Test Minimum-Evolution Tree
Or
Phylogeny | Construct/Test UPGMA Tree
Or
Phylogeny | Construct/Test Maximum Likelihood Tree
Or
Phylogeny | Construct/Test Maximum Parsimony Tree(s)
One of the most commonly used tests of the reliability of an inferred tree is Felsenstein's
(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 sequence 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,
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Part IV: Evolutionary Analysis
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, UPGMA, and Maximum Likelihood.
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Molecular Evolutionary Genetics Analysis
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 pseudorandom number generator. In each
bootstrap replicate, the desired quantity is estimated and the standard deviation of the
original values is 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).
Molecular Clock Tests
Tajima's Test (Relative Rate)
Molecular Clocks | Tajima’s Relative Rate 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 out-group. 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.
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:
1
10
20
seq1
A-AC-GGAT-AGGA-ATAAA
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Part IV: Evolutionary Analysis
seq2
seq3
AT-CC?GATAA?GAAAAC-A
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
Option
Sequence Data
Compl
ete
deletio
n
Pairwi
se
Deletio
n
1.
2.
3.
A
A
A
1.
2.
3.
A-AC-GGAT-AGGA-ATAAA
AT-CC?GATAA?GAAAAC-A
ATTCC-GA?TACGATA-AGA
C
C
C
GA
GA
GA
A GA A A A
A GA A C A
A GA A A A
Differences/Compari
sons
(1,
(1,
(2,3)
2)
3)
1/1
0/1
1/10
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.
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Molecular Evolutionary Genetics Analysis
Alignment Gaps and Sites with Missing Information
In MEGA, gap sites are ignored in the MP analysis, but there are three 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. The final option is Partial Deletion
which deletes the gaps assuming there are less than a certain percentage of gaps
(unambiguous).
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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. The third option is Partial Deletion (Site
coverage) as a percentage of unambiguous data (if there is less unambiguous data than the %
specified then it gets deleted).
Codon Positions
Check or uncheck the boxes to 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. 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 row, 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.
Tests of Selection
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 Ztest:
Z = (dN - dS) / SQRT(Var(dS) + Var(dN))
The level of significance at which the null hypothesis is rejected depends on the alternative
hypothesis (HA).
H0:
dN = dS
HA:
(a)
dN ≠ dS
(test of neutrality).
(b)
dN > dS
(positive selection).
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Molecular Evolutionary Genetics Analysis
(c)
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 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).
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Part IV: Evolutionary Analysis
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 yellow row indicates that you have a choice for that particular
attribute. The three primary sets of options available in this dialog box are:
Analysis
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).
Test Hypothesis
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 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 (HA):
H0:
dN = dS
HA:
(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
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).
Variance Estimation Method
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.
Substitution Model
In this set of options, you can choose various attributes of the substitution models for
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Molecular Evolutionary Genetics Analysis
DNA and protein sequences.
Substitutions Type
This is limited to Syn-Nonsynonymous.
Model
By clicking on the row of the currently selected model, you may select a stochastic
model for estimating evolutionary distance (click on the yellow 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).
Data Subset to Use
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), or you may use Partial Deletion (Site
coverage) as a percentage.
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 yellow row, 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.
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Part IV: Evolutionary Analysis
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:
Substitution Model
In this set of options, you choose various attributes of the substitution models for DNA
and protein sequences.
Substitutions Type
Here you are limited to Syn-Nonsyn.
Model
By clicking on the row currently selected model, you may select a stochastic model for
estimating evolutionary distance. 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).
Data Subset to Use
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 (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), or you may use Partial Deletion (Site
coverage) as a percentage.
Labeled Sites
This option is available only if some or all of the sites have associated labels. By
clicking on the row, 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.
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Molecular Evolutionary Genetics Analysis
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 yellow 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.
Data Subset to Use
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 (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), or you may use Partial Deletion (Site
coverage) as a percentage.
Codon Positions
Check or uncheck the boxes to 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 proteincoding regions into amino acid sequences and conduct the protein sequence analysis.
Labeled Sites
This option is available only if some or all of the sites have associated labels. By
clicking on the row, 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.
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
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Part IV: Evolutionary Analysis
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.
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Part V: Visualizing and Exploring Data and Results
Distance Matrix Explorer
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 MenuHC_Dist_Matrix_Exp_File_Menu
Display MenuHC_Dist_Matrix_Exp_Display_Menu
Average MenuHC_Dist_Matrix_Exp_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 are shown, 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: Has been replaced with an new system. Now simply move your mouse over
the divider between the sequence name and the first column of data. Your cursor will
change(to an arrow pointing left and right). Click and drag to resize the names.
Export Data
: This brings up the Exporting Sequence Data dialog box, which contains
options to control how MEGA writes the output data, available options are Text, MEGA, CSV,
and Excel.
The 2-Dimensional Data Grid
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
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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.
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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: This item 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".
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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, 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.
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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,
CSV, or Excel, with a choice of several formats.
Quit Viewer: This exits the Distance Data Explorer.
Sequence Data Explorer
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.
This explorer consists of a number of regions as follows:
Menu Bar
Data menuHC_Data_Menu_in_sequence_Data_Explorer
Search menu
Display menuHC_Display_Menu_in_Sequence_Data_Explorer
Highlight menuHC_Highlight_Menu_in_Sequence_Data_Explorer
Statistics menuHC_Stats_Menu_in_Sequence_Data_Explorer
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
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options to control how MEGA writes the output data, available options are Text, MEGA, CSV,
and Excel.
: This brings up the Exporting Sequence Data dialog box and sets the default output format
to MEGA.
: This brings up the Exporting Sequence Data dialog box and sets the default output format
to Excel.
: This brings up the Exporting Sequence Data dialog box and sets the default output format
to CSV (Comma separated values).
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 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).
Special: This dropdown allows for the selection of a special highlighting option.
CpG/TpG/CpA: if this button is pressed, then all sites which have a C followed by a G, T by G,
or C by A will be highlighted. You may also select a percentage of sequences which must
have these properties for a site to be counted.
Coverage: if this button is pressed, then you will enter a percentage. All the sites with this
percentage or less of ambiguous sites will be highlighted.
: This button provides the facility to translate codons in the sequence data into amino
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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.
Searching
: This button allows you to specify a sequence name to find. Search results are bolded and
the row is highlighted blue. MEGA first looks for an exact match to the name you specified, if
none exists it looks for names starting with what you provided, if no names start with the
provided search term, then MEGA looks for your search term anywhere in the names(rather
than just the start).
: This button allows you to specify a Motif to search for in the sequence data. This Motif
supports IUPAC codes such as R (for A or G) and Y (for T or C). MEGA highlights (in Yellow)
the first instance of this motif it finds.
and
: These buttons are only enabled if you have already searched for a Sequence
Name or Motif. By clicking the forward or backward button MEGA will search for the next or
previous search result (assuming there is more than one possible matches).
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
This allows you to explore the active data set, and establish various data attributes, and data
subset options. It also 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 (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
FileHC_Export_Data
_in_Sequence_Data
_Explorer
Brings up the Exporting
Sequence Data dialog box.
Translate/Untranslat
eHC_Translate_Untr
anslate_in_Sequenc
e_Data_Explorer
Translates protein-coding
nucleotide sequences into
protein sequences, and
back to nucleotide
sequences.
Select Genetic Code
TableHC_Select_Ge
netic_Code_Table_in
_Sequence_Data_Ex
plorer
Brings up the Select
Genetic Code dialog box, in
which you can select, edit or
add a genetic code table.
Setup/Select Genes
and
DomainsHC_Setup_
Select_Genes_Dom
ains_in_Sequence_
Data_Explorer_
Brings up the Sequence
Data Organizer, in which
you can define and edit
genes and domains.
Setup/Select Taxa
and
GroupsHC_Setup_S
elect_Taxa_Groups_
in_Sequence_Data_
Explorer
Brings up the Select/Edit
Taxa and Groups dialog, in
which you can edit taxa and
define groups of taxa.
Quit Data
ViewerHC_Quit_Dat
a_Viewer
Takes the user back to the
main interface.
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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.
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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 Menu.
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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.
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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.
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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 non-coding 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.
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Quit Data Viewer
Data | Quit Data Viewer
This command closes the Sequence Data Explorer, and takes the user back to main interface.
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.
Select Color: This option changes the color for highlighted sites. It is Yellow by default.
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.
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Restore Input Order
Display | Restore Input Order
Choosing this restores the order in Sequence Data Explorer to that in the input text file.
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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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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:
Symbo
l
Color
A
Yellow
G
Fuchsi
a
C
Olive
T
Green
U
Green
For amino acid sequences:
Symbo
l
Color
Symbol
Color
A
Yello
w
M
Yellow
C
Olive
N
Green
D
Aqua
P
Blue
E
Aqua
Q
Green
F
Yello
w
R
Red
G
Fuchs
ia
S
Green
H
Teal
T
Green
I
Yello
w
V
Yellow
K
Red
W
Green
L
Yello
w
Y
Lime
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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.
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Show Sequence Names
Display | Show Sequence Names
This option displays the full sequence names in Sequence Data Explorer
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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
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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
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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.
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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.
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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.
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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 dragging-and-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.
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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.
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Highlight Conserved Sites
Highlight | Conserved Sites
Use this command to highlight constant sites
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Highlight Variable Sites
Highlight | Variable Sites
Use this command to highlight variable sites sites.
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Highlight Singleton Sites
Highlight | Singleton Sites
Use this command to highlight singleton sites.
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Highlight Parsimony Informative Sites
Highlight | Parsim-Info Sites
Use this command to highlight parsimony-informative sites.
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Highlight 0-fold Degenerate Sites
Highlight | 0-fold Degenerate Sites
Use this command to highlight 0-fold degenerate sites.
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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.
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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:
Nucleotide CompositionHC_Nucleotide_Composition.
Nucleotide Pair FrequenciesHC_Nucleotide_Pair_Frequencies.
Codon UsageHC_Codon_Usage.
Amino Acid CompositionHC_Amino_Acid_Composition.
Use All Selected SitesHC_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.
Display results in Excel (XL) - Only effects outputs from the Statistics menu
Display results in Comma-Delimited (CSV) - Only effects outputs from the Statistics menu
Display results in Text Editor - Only effects outputs from the Statistics menu
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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).
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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 by
domain (if domains have been defined in Setup/Select Genes & Domains).
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Codon Usage
Statistics | Codon Usage
This command is visible only if the data contains protein-coding nucleotide sequences. MEGA
computes the percent codon usage and the RCSU values for each codon for all sequences
included in the dataset. Results will be displayed in by domain (if domains have been defined
in Setup/Select Genes & Domains).
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Amino Acid Composition
Statistics | Amino acid Composition
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 by
domain (if domains have been defined in Setup/Select Genes & Domains).
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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.
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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.
Tree Explorer
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 Menu
Image Menu
Sub-tree Menu
View Menu
Compute Menu
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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 tree-length. 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.
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File Menu (in Tree Explorer)
This menu has the following options:
Save Current Session: 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 (Newick): 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 (Newick): 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, 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.
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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 software 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 better. 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.
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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 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.
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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.
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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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.
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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.
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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.
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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.
Tree
Branch
Labels
Scale
Cutoff
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Tree tab (in Options dialog box)
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.
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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.
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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.
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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.
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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.
Alignment Explorer
Alignment Explorer
The Alignment Explorer provides options to (1) view and manually edit alignments and (2)
generate alignments using a built-in CLUSTALW implementation and MUSCLE program (for
the complete sequence or data in any rectangular region). The Alignment Explorer also
prodes 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 (and
MUSCLE) alignment algorithms are initiated, they will only 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.
You may adjust the width of the sequence name column by clicking on the line which
separates the sequence names column and the start of the data column and dragging.
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Aligning Sequences
In this tutorial, we will show how to create a multiple sequence alignment from protein
sequence data that will be imported into the alignment editor using different methods. All of
the data files used in this tutorial can be found in the MEGA\Examples\ folder (The default
location for Windows users is C:\Program Files\MEGA\Examples\. The location for Mac
users is $HOME/MEGA/Examples, where $HOME is the user’s home directory).
Opening an Alignment
The Alignment Explorer is the tool for building and editing multiple sequence alignments in
MEGA.
Example 2.1:
Launch the Alignment Explorer by selecting the Align | Edit/Build Alignment on the
launch bar of the main MEGA window.
Select Create New Alignment and click Ok. A dialog will appear asking “Are you
building a DNA or Protein sequence alignment?” Click the button labeled “DNA”.
From the Alignment Explorer main menu, select Data | Open | Retrieve sequences
from File. Select the "hsp20.fas" file from the MEG/Examples directory.
Aligning Sequences by ClustalW
You can create a multiple sequence alignment in MEGA using either the ClustalW or Muscle
algorithms. Here we align a set of sequences using the ClustalW option.
Example 2.2:
Select the Edit | Select All menu command to select all sites for every sequence in the
data set.
Select Alignment | Align by ClustalW from the main menu to align the selected
sequences data using the ClustalW algorithm. Click the “Ok” button to accept the
default settings for ClustalW.
Once the alignment is complete, save the current alignment session by selecting Data |
Save Session from the main menu. Give the file an appropriate name, such as
"hsp20_Test.mas". This will allow the current alignment session to be restored for
future editing.
Exit the Alignment Explorer by selecting Data | Exit Aln Explorer from the main menu.
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Aligning Sequences Using Muscle
Here we describe how to create a multiple sequence alignment using the Muscle option.
Example 2.3:
Starting from the main MEGA window, select Align | Edit/Build Alignment from the
launch bar. Select Create a new alignment and then select DNA.
From the Alignment Explorer window, select Data | Open | Retrieve sequences from a
file and select the “Chloroplast_Martin.meg” file from the MEGA/Examples directory.
On the Alignment Explorer main menu, select Edit | Select All.
On the Alignment Explorer launch bar, you will find an icon that looks like a flexing
arm. Click on it and select Align DNA.
Near the bottom of the MUSCLE - AppLink window, you will see a row called
Alignment Info. You can scroll through the text to read information about the Muscle
program.
Click on the Compute button (accept the default settings). A Progress window will keep
you informed of Muscle alignment status. In this window, you can click on the
Command Line Output tab to see the command-line parameters which were passed to
the Muscle program. Note: The analysis may complete so fast, that you won’t be able
to click on this tab or read it. The information in this tab isn’t essential, it’s just
interesting.
When the Muscle program has finished, the aligned sequences will be passed back to
MEGA and displayed in the Alignment Explorer window.
Close the Alignment Explorer by selecting Data | Exit Aln Explorer. Select No when
asked if you would like to save the current alignment session to file.
Obtaining Sequence Data from the Internet (GenBank)
Using MEGA’s integrated browser you can fetch GenBank sequence data from the NCBI
website if you have an active internet connection.
Example 2.4:
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From the main MEGA window, select Align | Edit/Build Alignment from the main
menu.
When prompted, select Create New Alignment and click ok. Select DNA
Activate MEGA’s integrated browser by selecting Web | Query Genbank from the main
menu.
When the NCBI: Nucleotide site is loaded, enter CFS as a search term into the search
box at the top of the screen. Press the Search button.
When the search results are displayed, check the box next to any item(s) you wish to
import into MEGA.
If you have checked one box: Locate the dropdown menu labeled Display
Settings (located near the top left hand side of the page directly under the tab
headings). Change its value to FASTA and then click Apply. The page will
reload with all the search results in a FASTA format
If you have checked more than one box: locate the Display Settings dropdown
(located near the top left hand side of the page directly under the tab headings).
Change the value to FASTA (Text) and click the Apply button. This will output
all the sequences you selected as a text in the FASTA format.
Press the Add to Alignment button (with the red + sign) located above the web address
bar. This will import the sequences into the Alignment Explorer.
With the data now displayed in the Alignment Explorer, you can close the Web Browser
window.
Align the new data using the steps detailed in the previous examples.
Close the Alignment Explorer window by clicking Data | Exit Aln Explorer. Select No
when asked if you would like the save the current alignment session to file.
Note: We have aligned some sequences and they are now ready to be analyzed. Whenever you
need to edit/change your sequence data, you will need to open it in the Alignment Editor and
edit or align it there. Then export it to the MEGA format and open the resulting file.
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Aligning coding sequences via protein sequences
MEGA 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 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.
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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.
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 displays the MUSCLE parameters dialog window, which is used
to configure MUSCLE 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
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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.
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 #
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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.
Part V: Visualizing and Exploring Data and Results
Menu Items
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.
Align by MUSCLE: 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 MUSCLE; to accept the parameters, press "OK". This initiates
the MUSCLE 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
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 unmark 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.
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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.
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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 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 readonly.
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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. There
are two formats to choose from: MEGA or FASTA formats. You will be requested to give a file
name to write the data to.
DNA Sequences: Use this item to specify that the input data is DNA. If DNA is 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.
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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 search 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 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.
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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.
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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.
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Appendix A: Frequently Asked Questions
How do I prevent the "MEGA Update Available" message showing up
Go to your MEGA installation directory C:\Program Files\MEGA4x1, then go to folder
Private\Ini and edit the ignore_updates.ini file. You should change the 0 to 1 for the file.
If you are running MEGA from a remote computer, this works also.
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Appendix A: Frequently Asked Questions
How can I ignore the current update available messag in MEGA's main window
By clicking the blue [X] link next to "MEGA Update Available" you will ignore the current new
version but be noticed of subsequent updates.
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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.
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Appendix A: Frequently Asked Questions
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 an option regarding the number of sites.
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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.
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Appendix A: Frequently Asked Questions
Menus in MEGA are so short; where are all the options?
Our aim in developing the objectively driven user-interface of MEGA has been a clutter-free
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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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.
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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.
Phylogeny
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 Data
menu.
Data Description window
This displays a summary of the currently active data set.
File Data
Open Data
Data | Activate Data for Analysis | Get Data from MEGA text File
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
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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, some of the file’s basic attributes will be displayed
the bottom of the main window. To open a different data file, close the currently active data
using the File | Close Data command first.
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.
Details
Click this button to view a list of files and directories along with
time stamp, size, and attribute information.
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Export Data
Data | 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.
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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.
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Close Data
Data | Close Data
This deactivates the currently open data file. Before issuing this command, save any
modifications that you wish to retain by using Session Saving (Data | Save Session).
This command is enabled only if a dataset is loaded in MEGA.
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Exit
Data | 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.
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Printer Setup
Data | Printer Setup
Choose this command to change the properties of your printer.
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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.
Data Menu
Data Menu
This allows you to explore the active data set, and establish various data attributes, and data
subset options. It also 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 Explorer
Data | Data Explorer
Data Explorers 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
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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.
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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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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-by-nucleotide 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.
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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.
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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.
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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.
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Molecular Evolutionary Genetics Analysis
Select Preferences
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.
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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.
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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.
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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.
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Compute Within Groups Mean
Distances | Compute Within Groups Means…
This computes the mean pairwise distances within groups of taxa. The within group 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.
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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 inter-populational diversity is given by
.
Coefficient of Differentiation
The estimate of the proportion of interpopulational diversity is given by
.
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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
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.
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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.
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.
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Bootstrap Test of Phylogeny
Phylogeny | Construct/Test Neighbor-Joining Tree
Or
Phylogeny | Construct/Test Minimum-Evolution Tree
Or
Phylogeny | Construct/Test UPGMA Tree
Or
Phylogeny | Construct/Test Maximum Likelihood Tree
Or
Phylogeny | Construct/Test Maximum Parsimony Tree(s)
One of the most commonly used tests of the reliability of an inferred tree is Felsenstein's
(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 sequence 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, UPGMA, and Maximum Likelihood.
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Interior Branch Test of Phylogeny
Phylogeny | Construct/Test Neighbor-Joining Tree
Or
Phylogeny | Construct/Test Minimum-Evolution Tree
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. Select test of phylogeny for either of these trees in the Analysis
Preferences dialog.
See Nei and Kumar (2000) (chapter 9) for further details.
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Display Saved Tree Session
Exploer/Editor | Display Saved Tree Session…
Use this command to display a previously saved Tree Explorer session (saved in a filename
with .MTS extension).
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Display Newick Trees from File
Explorer/Editor | 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).
Relative Rate Tests
Tajima's Test (Relative Rate)
Molecular Clocks | Tajima’s Relative Rate 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 out-group. 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.
Construct Phylogeny
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 mid-point rooting.
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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 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 tree.
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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 (parsimony-informative
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 close-neighbor-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)
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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).
Selection Menu
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.
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Codon Based Z-Test (large sample)
Distance | 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),
respectively. With this information, we can test the null hypothesis that H0: dN = dS using a Ztest:
Z = (dN - dS) / SQRT(Var(dS) + Var(dN))
The level of significance at which the null hypothesis is rejected depends on the alternative
hypothesis (HA)
H0:
dN = dS
HA:
(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).
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Codon Based Fisher's Exact Test
Distance | 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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Open Alignment Explorer
Data | Open Alignment Explorer
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.
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Query Databanks
Alignment | Query Databanks
Use this to open the MEGA web-browser to search the NCBI and other web sites for
sequence data.
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Show Web Browser
Alignment | Show Web Browser
Use this option to launch the MEGA Web Browser.
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View/Edit Sequencer Files
Data | 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.
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Index
Help | Index
This command provides access to the help file index and keyword searching facilities.
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About
Help | About…
This command will display the About dialog box showing the copyright, authors, and version
information for MEGA.
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.
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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.
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 subwindow 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-a-pencil 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.
Saving and Restoring Groups: You can save and restore which groups each taxa are stored
in. This can prevent you from needing to setup the groups each time. Normally you would
just save the session (using session saving). Although if you wanted to edit your data outside
of MEGA then you would need to use a MEG file and use this to restore the groups.
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.
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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
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.
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Appendix B: Main Menu Items and Dialogs Reference
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 inter-genetic domains. In addition, within or 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.
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Molecular Evolutionary Genetics Analysis
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.
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
378
Appendix B: Main Menu Items and Dialogs Reference
domain gene approach is unsuitable, you should assign a category to these sites, which can
be specified in addition to the groups and domains.
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Molecular Evolutionary Genetics Analysis
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
Add
Delete
Edit
View
Statistic
s
380
Description
Creates a new genetic code table. A code table editor will be shown
with the genetic code of the currently highlighted code table loaded.
Removes the highlighted genetic code from the list. Note that the
standard genetic code cannot be deleted.
Modifies the highlighted genetic code or its name. The code table
editor will be invoked for editing the genetic code.
Displays the highlighted genetic code in a printable format.
Displays the number of synonymous and non-synonymous sites for the
codons of the highlighted genetic code following the Nei-Gojobori
(1986) method. The degeneracy values for the first, second, and third
codon positions are displayed following Li et al. (1985).
Appendix C: Error Messages
Blank Names Are Not Permitted
As this error message suggests, you cannot leave the name of a sequence, taxa, domain, or
gene blank.
381
Molecular Evolutionary Genetics Analysis
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.
382
Appendix C: Error Messages
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).
383
Molecular Evolutionary Genetics Analysis
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.
384
Appendix C: Error Messages
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).
385
Molecular Evolutionary Genetics Analysis
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 the numbers 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
386
Appendix C: Error Messages
Gamma Distance Failed Because p > 0.99
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.
387
Molecular Evolutionary Genetics Analysis
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.
388
Appendix C: Error Messages
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.
389
Molecular Evolutionary Genetics Analysis
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.
390
Appendix C: Error Messages
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.
391
Molecular Evolutionary Genetics Analysis
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 Jukes-Cantor 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.
392
Appendix C: Error Messages
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.
393
Molecular Evolutionary Genetics Analysis
LogDet Distance Could Not Be Computed
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).
394
Appendix C: Error Messages
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.
395
Molecular Evolutionary Genetics Analysis
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 and 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.
396
Appendix C: Error Messages
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.
397
Molecular Evolutionary Genetics Analysis
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.
398
Appendix C: Error Messages
Not Yet Implemented
The task you requested was not activated. This function either was not being 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.
399
Molecular Evolutionary Genetics Analysis
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 option. All such pairs will
be shown in the Distance Matrix Dialog with a red n/c.
400
Appendix C: Error Messages
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).
401
Molecular Evolutionary Genetics Analysis
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).
402
Appendix C: Error Messages
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).
403
Molecular Evolutionary Genetics Analysis
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).
404
Appendix C: Error Messages
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.
405
Molecular Evolutionary Genetics Analysis
User Stopped Computation
You have aborted the current process by pressing the Stop process button on the progress
indicator.
406
Glossary
ABI File Format
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.
407
Molecular Evolutionary Genetics Analysis
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.
408
Glossary
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.
409
Molecular Evolutionary Genetics Analysis
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.
410
Glossary
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.
411
Molecular Evolutionary Genetics Analysis
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 branchand-bound search, which is described in detail in Kumar et al. (1993) and Nei and Kumar
(2000, page 123).
412
Glossary
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.
413
Molecular Evolutionary Genetics Analysis
ClustalW
ClustalW 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/).
414
Glossary
Codon
A codon is triplet of nucleotides that codes for a specific amino acid.
415
Molecular Evolutionary Genetics Analysis
Codon Usage
3
There are 64 (4 ) 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).
416
Glossary
Complete-Deletion Option
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).
417
Molecular Evolutionary Genetics Analysis
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.
418
Glossary
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.
419
Molecular Evolutionary Genetics Analysis
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.
420
Glossary
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).
421
Molecular Evolutionary Genetics Analysis
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.
422
Glossary
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.
423
Molecular Evolutionary Genetics Analysis
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 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.
424
Glossary
Domains
A domain is a continuous block of sites in a sequence alignment. A domain can be freestanding 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.
425
Molecular Evolutionary Genetics Analysis
Exon
A protein-coding gene typically consists of multiple coding regions, known as exons,
interspersed with non-coding DNA (introns)
426
Glossary
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 which the sequences and other information belong are extant or
extinct.
427
Molecular Evolutionary Genetics Analysis
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.
428
Glossary
Format command
A format command in a data file begins with! Format and contains at least the data type
included in the file.
429
Molecular Evolutionary Genetics Analysis
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.
430
Glossary
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 domains are automatically unselected.
However, a gene can be selected, with some of its domains unselected.
431
Molecular Evolutionary Genetics Analysis
432
Glossary
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 multi-gene family may be arranged into groups consisting of orthologous
sequences.
433
Molecular Evolutionary Genetics Analysis
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.
434
Glossary
Independent Sites
In a sequence alignment, all sites that have not been assigned to any gene or domain are
classified as independent.
435
Molecular Evolutionary Genetics Analysis
436
Glossary
Intron
Introns are the non-coding segments of DNA in a gene that are interspersed among the exons.
437
Molecular Evolutionary Genetics Analysis
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.
Labeled sites work independently of and in addition to genes and domains, thus allowing
complex subsets of sites to be defined easily.
438
Glossary
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 Tamura-Nei distance). Further information is in the
Maximum Composite Likelihood Method.
439
Molecular Evolutionary Genetics Analysis
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.
440
Glossary
Maximum Parsimony Principle
For any given topology, the sum of the minimum possible substitutions over all sites is known
as the tree length for that topology. The topology with the minimum tree length is known as
the Maximum Parsimony tree.
441
Molecular Evolutionary Genetics Analysis
Mid-point rooting
In the mid-point rooting method, the root of an unrooted tree is placed at the mid-point of the
longest distance between two taxa in a tree.
442
Glossary
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)
443
Molecular Evolutionary Genetics Analysis
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.
444
Glossary
mRNA
Protein-coding genes are first transcribed into messenger RNAs (mRNA), which are, in turn,
translated into amino acid sequences to make proteins.
445
Molecular Evolutionary Genetics Analysis
NCBI
An 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).
446
Glossary
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)80:20.59201,
weasel:18.87953)75:2.09460)50:3.87382,dog:25.46154);
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);
447
Molecular Evolutionary Genetics Analysis
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.
448
Glossary
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.
449
Molecular Evolutionary Genetics Analysis
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.
450
Glossary
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.
Coefficients wij’s for an external branch
where, mA and mB are the numbers of sequences in clusters A and B.
451
Molecular Evolutionary Genetics Analysis
Orthologous Genes
Two genes are said to be orthologous if they are the result of a speciation event.
452
Glossary
Out-group
An out-group is a sequence (or set of sequences) that is known to be a sister taxa to all other
sequences in the dataset.
453
Molecular Evolutionary Genetics Analysis
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.
454
Glossary
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.
455
Molecular Evolutionary Genetics Analysis
Polypeptide
A polypeptide is a chain of many amino acids.
456
Glossary
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.
457
Molecular Evolutionary Genetics Analysis
Protein parsimony
A Maximum Parsimony analysis on protein sequences is known as protein parsimony.
458
Glossary
Purifying selection
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.
459
Molecular Evolutionary Genetics Analysis
Purines
The nucleotides adenine (A) and guanine (G) are known as purines.
460
Glossary
Pyrimidines
The nucleotides cytosine (C) and thymine (T) are known as pyrimidines.
461
Molecular Evolutionary Genetics Analysis
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.
462
Glossary
463
Molecular Evolutionary Genetics Analysis
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.
464
Glossary
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.
465
Molecular Evolutionary Genetics Analysis
Site Label
The individual sites in nucleotide or amino acid data can be labeled to construct noncontiguous 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 Third Gene are labeled with a ‘+’ mark.
An underscore marks an absence of any labels.
!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.
466
Glossary
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
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.
467
Molecular Evolutionary Genetics Analysis
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.
468
Glossary
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.
469
Molecular Evolutionary Genetics Analysis
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.
470
Glossary
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.
471
Molecular Evolutionary Genetics Analysis
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.
472
Glossary
Topology
The branching pattern of a tree is its topology.
473
Molecular Evolutionary Genetics Analysis
Transition
A transition occurs when a purine is substituted by a purine, or a pyrimidine by a pyrimidine.
474
Glossary
Transition Matrix
A transition matrix specifies the probability of every possible substitution among the
nucleotides or amino acids.
475
Molecular Evolutionary Genetics Analysis
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).
476
Glossary
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.
477
Molecular Evolutionary Genetics Analysis
Transversion
A change from a purine to a pyrimidine, or vice versa, is a transversion.
478
Glossary
479
Molecular Evolutionary Genetics Analysis
Unrooted tree
An unrooted tree is one in which no assumption is made regarding the ancestor of all the taxa
in the tree.
480
Glossary
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.
481
References
Comeron 1995
Comeron JM (1995) A method for estimating the numbers of synonymous and nonsynonymous substitutions per
site. Journal of Molecular Evolution 41:1152-1159.
482
References
Dopazo 1994
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.
483
Molecular Evolutionary Genetics Analysis
Dayhoff 1978
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.
484
References
Dayhoff 1979
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.
485
Molecular Evolutionary Genetics Analysis
DeBry 1992
DeBry RW (1992) The consistency of several phylogeny-inference methods under varying evolutionary rates.
Molecular Biology and Evolution 9:537-551.
486
References
Eck and Dayhoff 1966
Eck RV & Dayhoff MO (1966) Atlas of Protein Sequence and Structure. National Biomedical Research
Foundation, Silver Springs, Maryland.
487
Molecular Evolutionary Genetics Analysis
Efron 1982
Efron B (1982) The Jackknife, the Bootstrap and Other Resampling Plans. CBMS-NSF Regional Conference
Series in Applied Mathematics, Monograph 38, SIAM, Philadelphia.
488
References
Estabrook et al. 1975
Estabrook GF, Johnson CS & McMorris FR (1975) An idealized concept of the true cladistic character.
Mathematical Biosciences 23:263-272.
489
Molecular Evolutionary Genetics Analysis
Felsenstein 1978
Felsenstein J (1978) Cases in which parsimony or compatibility methods will be positively misleading.
Systematic Zoology 27:401-410.
490
References
Felsenstein 1985
Felsenstein J (1985) Confidence limits on phylogenies: An approach using the bootstrap. Evolution 39:783-791.
491
Molecular Evolutionary Genetics Analysis
Felsenstein 1986
Felsenstein J (1986) Distance Methods: Reply to Farris. Cladistics 2:130-143.
492
References
Felsenstein 1988
Felsenstein J (1988) Phylogenies from molecular sequences: Inference and reliability. Annual Review of
Genetics 22:521-565.
493
Molecular Evolutionary Genetics Analysis
Felsenstein 1993
Felsenstein J (1993) Phylogeny Inference Package (PHYLIP). Version 3.5. University of Washington, Seattle.
494
References
Felsenstein and Kishino 1993
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.
495
Molecular Evolutionary Genetics Analysis
Fitch 1971
Fitch WM (1971) Towards defining the course of evolution: Minimum change for a specific tree topology.
Systematic Zoology 20:406-416.
496
References
Fitch and Margoliash 1967
Fitch WM & Margoliash E (1967) Construction of phylogenetic trees. Science 155:279 284.
497
Molecular Evolutionary Genetics Analysis
Goldman 1993
Goldman N (1993) Statistical tests of models of DNA substitution. Journal of Molecular Evolution 36:182-198.
498
References
Gu and Zhang 1997
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.
499
Molecular Evolutionary Genetics Analysis
Hedges et al. 1992
Hedges SB, Kumar S, Tamura K, & Stoneking M (1992). Human origins and analysis of mitochondrial DNA
sequences. Science 255:737-739.
500
References
Hendy and Penny 1982
Hendy MD & Penny (D) (1982) Branch and bound algorithms to determine minimal evolutionary trees.
Mathematical Biosciences 59:277-290.
501
Molecular Evolutionary Genetics Analysis
Hendy and Penny 1989
Hendy M D & Penny D (1989) A framework for the quantitative study of evolutionary trees. Systematic Zoology
38:297-309.
502
References
Hillis and Bull 1993
Hillis DM & Bull JJ (1993) An empirical test of bootstrapping as a method for assessing confidence in
phylogenetic analysis. Systematic Biology 42:182-192.
503
Molecular Evolutionary Genetics Analysis
Hillis et al. 1996
Hillis DM, Moritz C & Mable BK (1996) Molecular Systematics. 2 edition. Sunderland, MA: Sinauer Associates,
Inc.
504
References
Jones et al. 1992
Jones DT, Taylor WR & Thornton JM (1992) The rapid generation of mutation data matrices from protein
sequences. Computer Applications in the Biosciences 8: 275-282.
505
Molecular Evolutionary Genetics Analysis
Jukes and Cantor 1969
Jukes TH & Cantor CR (1969) Evolution of protein molecules. In Munro HN, editor, Mammalian Protein
Metabolism, pp. 21-132, Academic Press, New York.
506
References
Kimura 1980
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.
507
Molecular Evolutionary Genetics Analysis
Kishino and Hasegawa 1989
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.
508
References
Kumar et al. 1993
Kumar S, Tamura K & Nei M (1993) MEGA: Molecular Evolutionary Genetics Analysis. Pennsylvania State
University, University Park, PA.
509
Molecular Evolutionary Genetics Analysis
Kumar and Gadagkar 2001
Kumar S & Gadagkar SR (2001) Disparity Index: A simple statistic to measure and test the homogeneity of
substitution patterns between molecular sequences. Genetics 158:1321-1327.
510
References
Lake 1987
Lake JA (1987) A rate-independent technique for analysis of nucleic acid sequences: Evolutionary parsimony.
Molecular Biology and Evolution 4:167-191.
511
Molecular Evolutionary Genetics Analysis
Li 1993
Li W-H (1993) Unbiased estimation of the rates of synonymous and nonsynonymous substitution. Journal of
Molecular Evolution 36:96-99.
512
References
Li 1997
Li W-H (1997) Molecular Evolution. Sunderland, MA: Sinauer Associates.
513
Molecular Evolutionary Genetics Analysis
Li et al. 1985
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.
514
References
Maddison and Maddison 1992
Maddison WP & Maddison DR (1992) MacClade: Analysis of phylogeny and character evolution. Version 3.
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515
Molecular Evolutionary Genetics Analysis
Nei 1986
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.
516
References
Nei and Gojobori 1986
Nei M & Gojobori T (1986) Simple methods for estimating the numbers of synonymous and nonsynonymous
nucleotide substitutions. Molecular Biology and Evolution 3:418-426.
517
Molecular Evolutionary Genetics Analysis
Nei and Jin 1989
Nei M & Jin L (1989) Variances of the average numbers of nucleotide substitutions within and between
populations. Molecular Biology and Evolution 6:290-300.
518
References
Nei and Kumar 2000
Nei M & Kumar S (2000) Molecular Evolution and Phylogenetics. Oxford University Press, New York.
519
Molecular Evolutionary Genetics Analysis
Nei et al. 1976
Nei M, Chakraborty R & Fuerst PA (1976) Infinite allele model with varying mutation rate. Proceedings of
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520
References
Nei et al. 1985
Nei M, Stephens JC & Saitou N (1985) Methods for computing the standard errors of branching points in an
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521
Molecular Evolutionary Genetics Analysis
Nei et al. 1998
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
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522
References
Page and Holmes 1998
Page RDM & Holmes EC (1998) Molecular Evolution: A Phylogenetic Approach. Blackwell Science, Oxford,
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523
Molecular Evolutionary Genetics Analysis
Pamilo and Bianchi 1993
Pamilo P& Bianchi NO (1993) Evolution of the Zfx and Zfy, genes: Rates and interdependence between the
genes. Molecular Biology and Evolution 10:271-281.
524
References
Pamilo and Nei 1988
Pamilo P & Nei M (1988) Relationships between gene trees and species trees. Molecular Biology and Evolution
5:568-583.
525
Molecular Evolutionary Genetics Analysis
Penny and Hendy 1985
Penny D & Hendy MD (1985) The use of tree comparison metrics. Systematic Zoology 34:75-82.
526
References
Press et al. 1993
Press WH, Flannery BP, Teukolsky SA & Vetterling WT (1989) Numerical Recipes in Pascal: The Art of
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527
Molecular Evolutionary Genetics Analysis
Purdom et al. 2000
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.
528
References
Rzhetsky and Nei 1992
Rzhetsky A & Nei M (1992) A simple method for estimating and testing minimum evolution trees. Molecular
Biology and Evolution 9:945-967.
529
Molecular Evolutionary Genetics Analysis
Rzhetsky and Nei 1993
Rzhetsky A & Nei M (1993) Theoretical foundation of the minimum-evolution method of phylogenetic inference.
Molecular Biology and Evolution 10:1073-1095.
530
References
Saitou and Nei 1987
Saitou N & Nei M (1987) The neighbor-joining method: A new method for reconstructing phylogenetic trees.
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531
Molecular Evolutionary Genetics Analysis
Sankoff and Cedergren 1983
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
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532
References
Sharp et al. 1986
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.
533
Molecular Evolutionary Genetics Analysis
Sneath and Sokal 1973
Sneath PHA & Sokal RR (1973) Numerical Taxonomy. Freeman, San Francisco.
534
References
Sourdis and Krimbas 1987
Sourdis J & Krimbas C (1987) Accuracy of phylogenetic trees estimated from DNA sequence data. Molecular
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535
Molecular Evolutionary Genetics Analysis
Sourdis and Nei 1988
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.
536
References
Studier and Keppler 1988
Studier JA & Keppler KL (1988) A note on the neighbor-joining algorithm of Saitou and Nei. Molecular Biology
and Evolution 5:729-731.
537
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Swofford 1993
Swofford DL (1993) Phylogenetic Analysis Using Parsimony (PAUP), Version 3.1.1. University of Illinois,
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538
References
Swofford 1998
Swofford DL (1998) PAUP*: Phylogenetic Analysis Using Parsimony (and Other Methods) Sunderland, MA:
Sinauer Associates.
539
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Swofford et al. 1996
Swofford DL, Olsen GJ, Waddell PJ & Hillis DM (1996). Phylogenetic Inference. In Hiillis DM, Moritz D, and
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540
References
Tajima 1983
Tajima F (1983) Evolutionary relationship of DNA sequences in finite populations. Genetics 105:437-460.
541
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Tajima 1989
Tajima F (1989) Statistical methods to test for nucleotide mutation hypothesis by DNA polymorphism. Genetics
123:585-595.
542
References
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Tajima F (1993) Simple methods for testing molecular clock hypothesis. Genetics 135:599-607.
543
Molecular Evolutionary Genetics Analysis
Tajima and Nei 1982
Tajima F & Nei M (1982) Biases of the estimates of DNA divergence obtained by the restriction enzyme
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544
References
Tajima and Nei 1984
Tajima F & Nei M (1984) Estimation of evolutionary distance between nucleotide sequences. Molecular Biology
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545
Molecular Evolutionary Genetics Analysis
Takahashi and Nei 2000
Takahashi K & Nei M (2000) Efficiencies of fast algorithms of phylogenetic inference under the criteria of
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547
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550
References
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551
Molecular Evolutionary Genetics Analysis
Tanaka and Nei 1989
Tanaka T & Nei M (1989) Positive Darwinian selection observed at the variable-region genes of
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552
References
Tateno et al. 1982
Tateno Y, Nei M & Tajima F (1982) Accuracy of estimated phylogenetic trees from molecular data. I. Distantly
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553
Molecular Evolutionary Genetics Analysis
Tateno et al. 1994
Tateno Y, Takezaki N & Nei M (1994) Relative efficiencies of the maximum likelihood, neighbor-joining, and
maximum parsimony methods when substitution rate varies with site. Molecular Biology and Evolution
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554
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555
Molecular Evolutionary Genetics Analysis
Zhang and Gu 1998
Zhang J & Gu X (1998) Correlation between the substitution rate and rate variation among sites in protein
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556
References
Zhang et al. 1997
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557
Molecular Evolutionary Genetics Analysis
Zhang et al. 1998
Zhang J, Rosenberg HF & Nei M (1998). Positive Darwinian selection after gene duplication in primate
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References
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Molecular Evolutionary Genetics Analysis
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Tamura K, Dudley J, Nei M & Kumar S (2007) MEGA4: Molecular Evolutionary Genetics Analysis (MEGA)
software version 4.0. Molecular Biology and Evolution 24:1596-1599.
560
Glossary
T
tree editor:
561
Index
A
Compute standard error..............................
ABI File Format.....................................407
Bootstrap method......................239, 252
About BLAST..........................................61
Computing Statistical Quantities for
Nucleotide Sequences........................42
About CLUSTALW..................................58
About dialog..........................................374
Acknowledgements..................................4
Adding/Modifying Genetic Code Tables114
Aligning coding sequences via protein
sequences...................................58, 320
Alignment Builder............................54, 316
Alignment Explorer/CLUSTAL..............369
Alignment Menu....................................368
Computing the Gamma Parameter (a).198,
223
Constructing Trees and Selecting OTUs
from Nucleotide Sequences................32
Constructing Trees from Distance Data. .45
Convert To MEGA Format Main File Menu
............................................................92
Creating Multiple Sequence Alignments 27,
55, 317
Alignment Menu in Alignment Explorer. .65,
323
Cutoff Values Tab.................................308
Alignment session................................409
Data | Select Preferences.....349, 351, 352
Analysis Preferences dialog.................180
D
B
Data | Setup/Select Genes...126, 179, 274,
347
Blank Names Are Not Permitted...........381
Data | Translate/Untranslate.........123, 271
Bugs...........................................................
Data File Parsing Error.........................382
Reporting............................................24
Data menu............................120, 268, 344
C
Data Menu in Alignment Explorer. . .68, 326
Change Font.Display....................136, 284
Dataset.................................389, 397, 398
Close Data............................................341
Dayhoff 1979........................................485
CLUSTAL...............................................90
ClustalW...............................................414
Dayhoff and JTT distances Gamma rates
..........................................................224
CLUSTALW Options DNA......................59
Dayhoff distance...........................484, 487
CLUSTALW Options Protein..................60
Dayhoff Distance Could Not Be Computed
..........................................................383
Codon...................................335, 336, 380
Codon.........................................................
inclusion/exclusion. . .236, 239, 242, 246,
257, 259
Dayhoff Model......................................223
Disclaimer.................................................2
Disparity Index......................................424
Color Cells....................................131, 279
Display | Restore Input Order.......129, 277
Common Features..................................72
Display | Show..............................130, 278
Common Sites......................................396
Display | Show Group Names.......135, 283
Composition Distance...........................418
Display | Sort Sequences..............137, 285
Compute Between Groups Means........359
Display Menu in Alignment Explorer......66,
324
Compute Menu.....................................316
Compute Pairwise................................354
Display Newick Trees from File......90, 363
Distance Correction Failed...................393
562
Index
Distance Data Subset Selection...........181
Gamma distance..................................225
Distance Display Precision...................262
Gene Names Must Be Unique..............388
Distance Matrix Dialog..................400, 404
General Comments on Statistical Tests248
Distance Model Options........................238
Group Name.................................138, 286
Distances | Choose Model....................353
Distances | Compute Overall Mean......355
Groups.....85, 89, 125, 139, 158, 264, 273,
287, 346, 356, 358
Distances Menu....................................352
Gu and Zhang 1997..............................499
Divergence Time...........................306, 315
H
Divergence Time Dialog Box................309
Help Index............................................373
DNA......................................128, 183, 276
Help menu........................................6, 372
Do BLAST Search..................................62
Highlight | Parsim-Info Sites..........145, 293
Domains.......................................152, 300
Highlight 0-fold Degenerate Sites. 146, 294
Domains Cannot Overlap.....................384
Highlight 2-fold Degenerate Sites. 147, 295
E
Highlight 4-fold Degenerate Sites. 148, 296
Edit | Copy............................................174
Highlight Conserved Sites.............142, 290
Edit | Undo............................................176
Highlight Menu..............................141, 289
Edit menu.......................................47, 159
Highlight Singleton Sites...............144, 292
Edit Menu in Alignment Explorer.....67, 325
Highlight Variable Sites.................143, 291
EMF......................................................305
Highlighted Sites...........................154, 302
Equal Input Correction Failed...............385
Hillis et al. 1996....................................504
Equal Input Model.................................221
How can I ignore the current update
available messag in MEGA s main
window..............................................331
Equal Input Model Gamma...................199
Equal Input Model Gamma rates and
Heterogeneous Patterns...................213
Equal Input Model Heterogeneous
Patterns.............................................225
Estimating Evolutionary Distances from
Nucleotide Sequences........................30
How do I prevent the MEGA Update
Available message showing up........330
I
Include Codon Positions.......................348
Include Sites Option..............................255
Export Data...........................127, 275, 339
Incorrect Command Used.....................390
Export/Print Distances..........................266
Insertions/deletions.......................244, 254
Exporting Sequence Data dialog. .122, 270
Introduction to Walk Through MEGA......25
F
Intron..............................................83, 377
Feature List............................................11
Intron Property........................................82
File Menu..............................................344
J
Fisher's Exact Test.....................................
Jones et al. 1992..................................505
Selection...........................................368
Jukes-Cantor........................226, 228, 392
Formats..................................................86
K
G
[email protected]
Gamma.........................................200, 387
L
Leaf taxa...............................................303
563
Molecular Evolutionary Genetics Analysis
Li 1993..................................................512
quit....................................................159
Li 1997..................................................513
OTUs............................................239, 366
LogDet Distance Could Not Be Computed
..........................................................394
P
M
Page and Holmes 1998........................523
Managing Taxa With Groups..................41
Pairwise-Deletion..................................252
Max-mini branch-and-bound search.....440
Pattern Menu........................................186
Maximum Composite Likelihood...198, 439
Phenylalanine.........................................79
Maximum Composite Likelihood Gamma
Rates and Heterogeneous Patterns. .219
Phylogenetic 359, 444, 497, 503, 530, 535,
536, 553
Maximum Composite Likelihood
Heterogeneous Patterns...................213
Phylogeny | Any....................................302
Maximum Composite_Likelihood Gamma
..........................................................208
Mean Diversity............................................
Interpopulational Diversity.................357
MEGA Software Development Team........5
Minimum Evolution.......................241, 364
Missing.......................................................
data...................................................395
Data..................................................374
MP................................248, 249, 365, 413
N
NCBI.....................................................446
Nei et al. 1998......................................522
Neighbor-Joining..................................363
Newick Format......................................447
Notations Used.......................................25
NTaxa...............................................81, 87
Nucleotide Pair Frequencies 150, 184, 298,
450
Nucleotide-by-nucleotide......................186
Number.................................................386
Number.......................................................
0-fold.................................230, 232, 234
O
OLS branch length estimates................451
P-distance.............................................220
Phylogeny | Bootstrap Test...........250, 360
Phylogeny | Display Saved Tree
Session.Use......................................362
Phylogeny | Neighbor-Joining...............250
Poisson Correction distance.................222
Poisson Correction Failed.....................401
Polypeptide...........................................456
Preface.....................................................3
Print......................................................342
Printer Setup.................................304, 343
Protein parsimony.................................458
Purdom et al. 2000...............................528
Q
Query Databanks..................................370
Quit Data Viewer...........................128, 276
R
Reopen Data........................................340
RNA......................................................345
RSCU...................................................416
Rules..........................................76, 77, 78
S
Search Menu in Alignment Explorer69, 327
Select Genetic Code Table. .117, 124, 272,
350
Sequence Data Explorer...............148, 296
Open Data............................................337
Sequencer Menu in Alignment Explorer.70,
328
Open Saved Alignment Session.............50
Show..........................................................
Options dialog.............................................
statistics/frequency............................313
564
Index
Show Sequence Names...............134, 282
Show Web Browser..............................371
Sites.....................................................333
Sort Sequences By Sequence Name. .141,
289
Tamura-Nei distance Gamma rates and
Heterogeneous patterns....................215
Tamura-Nei distance Heterogeneous
Patterns.............................................211
Taxa...........................................................
Special Symbols.....................................78
categorize.........................................156
SQRT............................................255, 367
following........................................83, 87
Staden..................................................467
name.................................................265
Statistical Attributes..............................115
Taxa Names...........................................75
Statistics...............................................332
Taxa/Group Organizer..................140, 288
Statistics | Codon Usage.......151, 185, 299
Taxon............................................157, 312
Statistics | Nucleotide Composition......149,
297
Removing..........................................375
Taxon Name tab...................................314
Statistics | Use All Selected Sites. 153, 301
Test of Positive Selection.......................40
Status Bar.............................118, 154, 266
Tests | Interior Branch Test...........249, 361
Subtree Drawing Options (in Tree
Explorer)...........................................307
Tests of the Reliability of a Tree Obtained
............................................................36
Swofford 1998......................................539
Swofford et al. 1996..............................540
Text Editor....161, 162, 168, 169, 170, 172,
177, 178, 179
T
Title.........................................................72
Tajima...................191, 252, 260, 363, 366
Toolbars in Alignment Explorer.......62, 321
Tajima 1989..........................................542
Topological distance.............................472
Tajima and Nei 1982.............................544
Trace Data File Viewer/Editor.................49
Tajima Nei distance Gamma rates........203
Transition/transversion.182, 188, 189, 192,
194, 196, 201, 204
Tajima Nei Distance Gamma Rates and
Heterogeneous patterns....................214
Transitions + Transversions..................190
Tajima Nei Distance Heterogeneous
patterns.............................................208
Tree............................................................
Tajima-Nei Distance Could Not Be
Computed.........................................402
Tree Data...............................................89
Takahashi and Nei 2000.......................546
U
Takezaki et al. 1995..............................547
Unexpected Error.................................405
Tamura.................................................403
Unique ASCII........................................391
Tamura 3 parameter Gamma rates and
Heterogeneous patterns....................217
Updates..................................................23
Tamura 3 parameter Heterogeneous
patterns.............................................209
User Stopped Computation..................406
Tamura 3-parameter Gamma...............206
Tamura and Kumar 2002......................550
Tamura et al. 2007...............................560
Bifurcating.........................................410
Tree Explorer........................................311
Use Identical Symbol....................133, 281
Using MEGA in the classroom................22
V
View......................................................118
View menu....................................310, 337
565
Molecular Evolutionary Genetics Analysis
View/Edit Sequencer Files....................372
Writing....................................................73
W
Y
Web Explorer Tab Alignment Explorer....50
Yang 1997............................................555
Web Menu in Alignment Explorer. . .71, 329
Yeast mitochondrial..............................112
What s New in Version 3.0.......................9
Z
Windows...............................................7, 9
Z-statistic..............................................334
Windows Clipboard.......................173, 175
Zhang and Gu 1998..............................556
Words.....................................................74
ZIP file......................................................8
Working With Genes and Domains.........38
Zuckerkandl and Pauling 1965.............559
566
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