The Supertree Toolkit 2

The Supertree Toolkit 2
Davis, K. and Hill, J. (2014) The Supertree Toolkit 2:A new and
improved software package with a Graphical User Interface for
supertree construction. Biodiversity Data Journal. ISSN 13142836
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Biodiversity Data Journal 2: e1053
doi: 10.3897/BDJ.2.e1053
Software description
The Supertree Toolkit 2: a new and improved
software package with a Graphical User Interface
for supertree construction
Jon Hill†, Katie E Davis‡
† Imperial College London, London, United Kingdom
‡ University of Bath, Bath, United Kingdom
Corresponding author: Jon Hill ([email protected])
Academic editor: Matthew Yoder
Received: 10 Jan 2014 | Accepted: 25 Mar 2014 | Published: 26 Mar 2014
Citation: Hill J, Davis K (2014) The Supertree Toolkit 2: a new and improved software package with a Graphical
User Interface for supertree construction. Biodiversity Data Journal 2: e1053. doi: 10.3897/BDJ.2.e1053
Abstract
Building large supertrees involves the collection, storage, and processing of thousands of
individual phylogenies to create large phylogenies with thousands to tens of thousands of
taxa. Such large phylogenies are useful for macroevolutionary studies, comparative biology
and in conservation and biodiversity. No easy to use and fully integrated software package
currently exists to carry out this task. Here, we present a new Python-based software
package that uses well defined XML schema to manage both data and metadata. It builds
on previous versions by 1) including new processing steps, such as Safe Taxonomic
Reduction, 2) using a user-friendly GUI that guides the user to complete at least the
minimum information required and includes context-sensitive documentation, and 3) a
revised storage format that integrates both tree- and meta-data into a single file. These
data can then be manipulated according to a well-defined, but flexible, processing pipeline
using either the GUI or a command-line based tool. Processing steps include standardising
names, deleting or replacing taxa, ensuring adequate taxonomic overlap, ensuring data
independence, and safe taxonomic reduction. This software has been successfully used to
store and process data consisting of over 1000 trees ready for analyses using standard
supertree methods. This software makes large supertree creation a much easier task and
provides far greater flexibility for further work.
© Hill J, Davis K. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY
4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are
credited.
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Hill J, Davis K
Keywords
Supertree, phylogeny, data curation, meta-data
Introduction
Supertrees are large phylogenies created by amalgamating anywhere from tens to
thousands of smaller source phylogenies. A number of algorithms exist for this, the most
widely used being Matrix Representation with Parsimony (Ragan 1992). These algorithms
have varying extents of software implementation again with MRP being the most
commonly implemented. Supertrees have been created for a wide variety of taxonomic
groups, including birds (Cornwallis et al. 2010, Davis 2008), dinosaurs (Lloyd et al. 2008),
angiosperms (Davies et al. 2004) and marsupials (Cardillo et al. 2004). Large phylogenies
are useful for answering questions in macroevolution (e.g. Lloyd et al. 2008), comparative
biology (e.g. Edwards et al. 2007), biodiversity (Crozier et al. 2005, Helmus et al. 2007)
and conservation (e.g. Isaac et al. 2007). However, large-scale supertrees are not
straightforward to construct. Collecting, storing and processing the source trees which are
the input to any supertree algorithm is non-trivial as a number of important considerations
must be made concerning data quality. These issues with supertree analysis,
including data independence, taxonomic overlap, and consistent taxonomy have been
discussed elsewhere at length (Bininda-Emonds et al. 2004, Bininda-Emonds et al. 2005,
Gatesy 2004). Given the potential large size of datasets and the amount of processing that
must be done prior to the supertree analysis being carried out this is not an easy task.
There is therefore a need for a comprehensive software package that can carry out this
prior processing and can preferably store data in a well defined manner.
Some workers have written and made available scripts that carry out one or more of the
required processing steps (e.g. Bininda-Emonds et al. 2004, Bininda-Emonds et al. 2005,
Davis and Hill 2010). These scripts have a number of drawbacks such as the lack of a user
friendly interface and lack of optimisation resulting in slow, computationally intensive
analyses that take many days or even weeks to run for large datasets. Other issues
include data format conversion between processing steps, for example converting
from Newick-based tree strings to NEXUS format. In addition none of these software
scripts include methodologies for collecting metadata – a key part of a robust and rigorous
processing pipeline. An attempt to mitigate these issues led to the creation of the Supertree
Toolkit (STK) (Davis and Hill 2010); a collection of Perl scripts designed to carry out the
prior processing required for supertree construction. This package was the first to use both
source trees alongside their metadata to perform the processing. This software, however,
was difficult to use (command-line only) and had only a rudimentary GUI for creating
metadata. The storage mechanisms chosen required strict naming conventions and
therefore was somewhat fragile. Moreover, the metadata was optional for most of the
processing pipeline, thereby negating its full value. The original STK (Davis and Hill 2010),
The Supertree Toolkit 2: a new and improved software package with a Graphical ...
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whilst a step in the right direction, did not therefore meet the requirements for an easy-touse, rigorous method to collect both metadata and data.
Here, we present the next version of the Supertree Toolkit that builds on the experience of
the first version. We have rewritten all code and designed the software around a user
interface that can carry out both data collection and processing. It contains a number of
additional features over the original software which are 1) new processing steps, such as
Safe Taxonomic Reduction, 2) user-friendly GUI that guides the user to complete at least
the minimum information required and includes context-sensitive documentation, and 3) a
revised storage format that integrates both tree- and meta-data into a single file. We will
first detail the storage mechanism, based on RelaxNG XML, and the user interface
features. We then cover the available processing pipeline steps and show some examples
of their use.
Project description
Title: Supertree Toolkit (STK)
Design description: The STK consists of three components: a Python module, a Graphical
User Interface (GUI), and a Command Line Interface (CLI). The python module contains all
processing, importing and exporting functions. These functions deal with the Phyml format
(see below) and are available in any Python environment by importing the
supertree_toolkit module. The GUI and CLI then import this Python module and hook it to
the interface by processing user options. In this way the core functionality can be tested by
using standard unit test infrastructure and the interfaces are cleanly separated. A test suite
of over 375 tests is included in the source code which benchmark the expected
performance of the software.
User interface
There are two user interfaces: a GUI for data entry and processing, and a CLI for data
processing. The latter is useful for dealing with large datasets. The GUI is based on
Diamond (Ham et al. 2009), which was originally designed for entering user options for
numerical modelling software. We have extended the capabilities of Diamond to be
suitable for entering phylogenetic data. A number of specific plugins have been created to
import source trees and manage bibliographic sources. We use the BibTeX format for
importing and exporting bibliographic information, which is widely supported by references
managers. We have also added tools to carry out data processing based on those in the
original STK (Davis and Hill 2010) and extending them to include safe taxonomic reduction
(Wilkinson 1995). The GUI is split into two vertical panes; the left contains a hierarchical
view of the data. Each row in this hierarchy is an element, which can contain a number of
nested elements. Elements at the same level in the hierarchy can be copy and pasted to
aid data entry. The right-hand side is split into three horizontal panes (Fig. 1). The
uppermost pane displays context-sensitive help, the middle pane allows for data entry and
the lower-most pane contains user comments where available. All processing functions are
Hill J, Davis K
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available in the GUI and use specifically designed interfaces to allow the user to choose
options and output processing steps to the file. The command line interface contains the
processing steps as sub-commands. Each processing step then contains the options
specific to this function. As with the GUI, documentation is available using standard flags
and options.
Figure 1.
The STK GUI, based on Diamond (Ham et al. 2009). The GUI is split into two main panes, with the
left being used to store data and the right showing the context-based help (top), data entry (middle)
and comments (bottom).
We have maintained all the previous functionality of the previous version of the STK which
are detailed in Davis and Hill (2010). We therefore restrict ourselves to a brief description
of the functionality and describe new functionality in detail (Safe Taxonomic Reduction).
Metadata and file format
XML is an ideal way to store structured metadata. We build on the methods used by Spud
(Ham et al. 2009), using the RelaxNG method to create a data schema. This schema
dictates what information the user interface shows – the options in the left hand side of the
GUI (Fig. 1) are generated on the fly from this schema – and aids the user in two ways.
First, data can be defined as required or optional within the schema and the interface
highlights required data accordingly. Second, context-sensitive documentation is
embedded in the schema and is shown in the user interface. The user does not interact
directly with the schema, but the schema dictates which data can be stored and what is
shown in the user interface. The base schema is a human readable file in compact
RelaxNG format. It is this file where required and optional GUI elements can be added as
well as context-senstivie documentation. Software (spud-preprocess) distributed as part of
Spud (Ham et al. 2009) then transforms this into a full XML file. The two files (one a .rnc
the other a .rng) are distributed along with the GUI to generate the user interface. The GUI
then reads this XML files (the .rng file otherwise known as the schema) to display options
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to the user. The file the user saves is therefore linked to this schema as the schema
dictates the data that can be displayed and entered via the GUI and hence saved in a file.
There are a number of other file formats that allow the user to store both trees and their
metadata, such as NexML (Vos et al. 2012) and PhyloXML (Han and Zmasek 2009). We
have written the code so that the GUI can be automatically generated from our XML
schema, but not NeXML or PhyloXML, however parsers to import from and export to these
file formats will be added in future versions. The schema used here is easily extensible and
was designed with initiatives such as MIAPA in mind, which was used to define the terms
used, though not in a formal manner.
Each dataset has a name and contains a number of "Sources" (Fig. 2). Each source is a
publication and includes the bibliographic information, followed by one or more "Source
Trees". Each "Source Tree" then contains the tree string, the characters and methods used
to create the tree, along with information on fossil taxa and other metadata (figure number,
legend, etc.). Optional information includes conservation status, stratigraphic information,
synonyms and database accession numbers. This approach allows copy and pasting of
"Sources" between dataset via the GUI, easy navigation of the whole data structure as well
as the meta- and source tree data being displayed alongside each other. This is in contrast
to the previous version where files were distributed within sub-folders on disk and the tree
and meta-data were contained in separate files (Davis and Hill 2010).
Figure 2.
Data structure of the STK metadata. Each project consists of several sources, which in turn contain
bibliographic information and one or more source trees. The blue boxes show the hierarchy for a
single source tree. The data structure has been simplified here and more meta data can be stored
for each source tree.
The result of this schema is a single XML datafile that contains all metadata and source
data required. This file is termed a Phylml (Phylogenetic Meta Language), which can be
parsed by any standard XML parser.
Hill J, Davis K
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Processing functions
There are a number of processing functions included in the STK. These can be chained
together to construct a processing pipeline to collect, curate and process data (Fig. 3). The
processing functions are:
Figure 3.
Example of a processing pipeline that can be created with the STK. Data are collected and then are
put through the processing pipeline in order to create a matrix. The resulting matrix (in either Nexus
format (.nex) or Hennig format (.tnt)) can then be analysed in any suitable software such as PAUP*
(Swofford 2003) or TNT (Goloboff et al. 2008).
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Data summary – produce text summary of data, such as number of taxa, trees and
characters.
Clean data – check data and remove redundant data, such as non-informative
trees.
Permute all trees – remove non-monophyly from trees.
Substitute taxa – perform substitutions or deletions of taxa.
Data independence check – check that all source trees are independent of each
other.
Data overlap – check the taxonomic overlap (Fig. 4).
Replace genera – replace generic-level taxa with polytomies of all species in that
genus that are in the dataset.
Create matrix – create a MRP matrix (Baum and Ragan coding) of the dataset.
Create subset – create a subset of this dataset, e.g. only certain years or data
types.
STR – perform safe taxonomic reduction on the dataset. This is new functionality
over the previous version of the STK (Davis and Hill 2010)
The Supertree Toolkit 2: a new and improved software package with a Graphical ...
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Figure 4.
Result of the taxonomic overlap check which highlights which source trees are not sufficiently well
connected to the rest of the dataset.
Data summary
This function creates a text summary of the data. The summary includes a taxa list, years
of publication, characters used, and analyses used.
Clean data
Before and during processing trees may become uninformative (i.e. contain no clades), for
example after substitution of taxa, or when dealing with polyphyletic taxa. This function
checks that the data are suitable for processing and removes uninformative trees (and
sources if they contain no trees) and should be run regularly on data between processing
steps.
Permute trees
When creating supertrees at species level digitised trees need to account for the fact that
some species may be polyphyletic. There are no formal mechanisms for dealing with this
so taxa can be encoded with a '%d' sign to designate them as polyphyletic (where d is a
consecutive integer for each taxon). The 'permute trees' function generates all possible
permutations of these trees to enable a consensus tree of some kind to be created.
Substitute taxa
One of the most onerous tasks of supertree creation is ensuring a consistent taxonomy is
used throughout. This requires the removal of synonyms, mis-spellings and other naming
errors. The 'sub taxa' function allows substitution and deletion of taxa whilst maintaining
the tree structure. Substitutions are aware of polyphyletic taxa and will collapse
superfluous nodes when deleting taxa. This function is used throughout the processing.
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Hill J, Davis K
Data independence
It has previously been noted that all data included in supertree analyses should be
independent of each other. Here, we defined non-independent data as being datasets that
contain a subset of the same taxa and use identical characters. This function flags source
trees that are subsets of others (and can automatically remove them if required) and flags
those that are identical (i.e. same taxa and characters).
Replace genera
This is one of the final steps of the pipeline. After all processing some taxa at genus level
may be left in the source trees. This function replaces those genera with a polytomy of
species already in the dataset. Note that this assumes a species level tree is to be created
and this step can be omitted if this is not the case.
Data overlap
In order to create a supertree all source trees should exhibit sufficient taxonomic overlap
(Sanderson et al. 1998). Data overlap checks this with a user defined overlap (minimum is
two) and can present the results in two graphical formats (e.g. Fig. 4).
Create subset
One of the novelties of the STK is that it can be used to create subsets of the whole
dataset, based on the metadata. For example, all trees that used molecular character can
be extracted and used to create a new dataset. Similarly publication year, author, or
analysis type can all be used to create subsets. These can be used to create independent
supertrees and the effect of including, say, only molecular data, can be compared to the
supertree generated from the whole dataset.
Create matrix
One of the key functions of the STK is to create a matrix for supertree analysis from the
input source trees. This function can generate a matrix in a number of formats and also
output a single treefile containing all trees in the dataset.
Safe Taxonomic Reduction (STR)
A new function for this version is Safe Taxonomic Reduction (STR). This is the only new
functionality in this version over the previous version (Davis and Hill 2010). STR is as
detailed by Jeffery and Wilkinson (2004), but has been optimised for use on MRP
matrices as an assumption is made that the data only contain 0, 1 or ? as characters. This
substantially speeds-up computation and can process very large datasets consisting of
several thousand taxa, although run times are still days, rather than minutes. STR
identifies those taxa that give no extra phylogenetic information and recommends their
removal from the matrix. Wilkinson (2001) describes several categories of taxa that the
STR algorithm identifies. Of concern are the category "C" taxa where their removal from
the matrix is "safe" as they do not provide any additional information, but can be placed
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back into the supertree post analysis. The STK generates a new matrix as well as a text
file detailing the categories of all taxa, as well as identifying those that can be safely
removed. In addition, a substitution file can also be generated to put category C* back into
the final supertree once generated.
Funding: KED was funded by BBSRC grant (BB/K006754/1) and a Systematics
Association SynTax grant ("Building the arthropod supertree interactively: Malacostracan
crustaceans as a test case"2010/11 funding round awarded to Matthew Wills and Mark
Wilkinson).
Web location (URIs)
Homepage: http://supertreetoolkit.org/
Download page: https://launchpad.net/supertree-toolkit/+download
Bug database: https://bugs.launchpad.net/supertree-toolkit/+bugs
Technical specification
Platform: Linux, Windows, MacOS X
Programming language: Python
Repository
Type: bzr
Browse URI: https://code.launchpad.net/supertree-toolkit
Location: lp:supertree-toolkit
Usage rights
Use license: Other
IP rights notes: GNU GPL v3
Additional information
The STK is available from Launchpad (http://launchpad.net/supertree-toolkit). There are
two main bzr branches: a stable release version and the development version (trunk).
Contributors are expected to branch trunk, develop their new feature and request a merge
back into trunk. We encourage all such contributions. STK is released in GPLv3 and is
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Hill J, Davis K
available as source code, via Launchpad's PPA system, as a Windows and MacOS
X binary.
A full user manual, including a tutorial and data for the tutorial are available from the
Launchpad website.
In future we aim to integrate web-based taxonomy databases to aid taxonomy and
nomenclature standardisation. We are also developing a website to release all data that
have been collected thus far. The STK will be integrated into that online resource. Finally,
we intend to develop a simple tree editing and visualisation GUI such that no external
software is required for the whole processing pipeline.
Acknowledgements
The authors wish to thank Steve Mitchell, Cyrille Delmer and Matthew Wills (all at the
University of Bath) for help with testing and bug reporting. We would also like to thank Carl
Boettiger, Karen Cranston, Graeme Lloyd and Matthew Yoder for comments that helped
improve the manuscript.
Author contributions
JH wrote the Supertree Toolkit software and drafted the manuscript. KED drafted the
manuscript and designed the software.
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Supplementary material
Suppl. material 1: Manual and tutorial data
Authors: Jon Hill and Katie Davis
Data type: Mix: PDF, .nex and .phyml
Brief description: The STK User manual and tutorial dataset.
Filename: Tutorial_and_Manual.zip - Download file (1.32 MB)
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