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A Practical Guide to Phylogenetics for Nonexperts
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Agalma: an automated phylogenomics workflow.

Casey W Dunn1, Mark Howison, Felipe Zapata

  • 1Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island, USA. casey_dunn@brown.edu.

BMC Bioinformatics
|November 21, 2013
PubMed
Summary
This summary is machine-generated.

Agalma is a new automated tool that simplifies complex phylogenomic analyses. It constructs gene sequence matrices from transcriptome data, enabling reproducible and extendable phylogenetic studies.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Transcriptome data are increasingly vital for phylogenetic studies, offering cost-effective gene sequences.
  • Implementing phylogenomic studies is computationally intensive and often manual, hindering reproducibility.
  • Existing methods lack standardized tools, making analyses difficult to compare and extend.

Purpose of the Study:

  • To introduce Agalma, an automated tool for constructing matrices in phylogenomic analyses.
  • To streamline the process of phylogenomic data analysis and improve reproducibility.
  • To facilitate methods development and optimization within phylogenomic studies.

Main Methods:

  • Agalma processes raw transcriptome data to generate annotated assemblies and aligned gene sequence matrices.
  • It incorporates data from various sources, including external transcriptomes and genomes.
  • The tool is built on the BioLite bioinformatics framework, ensuring provenance tracking and detailed diagnostics.

Main Results:

  • Agalma automates the construction of phylogenomic matrices, including preliminary phylogenies and diagnostics.
  • It provides detailed assessments of intermediate and final analysis steps.
  • The tool supports the incorporation of diverse sequence data for comprehensive analyses.

Conclusions:

  • Agalma enables unambiguous implementation and description of complex phylogenomic analyses via high-level commands.
  • It enhances the reproducibility, modification, and extension of phylogenetic studies.
  • The modular workflow and bundled test data facilitate methods development and optimization.