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A Practical Guide to Phylogenetics for Nonexperts
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Benchmark datasets and software for developing and testing methods for large-scale multiple sequence alignment and

C Randal Linder1, Rahul Suri, Kevin Liu

  • 1Integrative Biology, University of Texas; University of Texas; The University of Texas and Microsoft Research New England, and The Department of Computer Science, University of Texas at Austin.

Plos Currents
|November 30, 2010
PubMed
Summary
This summary is machine-generated.

Researchers created benchmark datasets and tools to evaluate computational methods for evolutionary relationship estimation. This resource aids in developing new alignment and tree inference methods for complex biological data.

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Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Accurate estimation of evolutionary relationships is crucial in biology.
  • Existing computational methods face challenges with complex datasets, including multiple sequence alignment and phylogeny estimation.
  • There is a need for standardized benchmarks to evaluate and improve these methods.

Purpose of the Study:

  • To provide a comprehensive collection of benchmark datasets and software tools.
  • To facilitate the evaluation of accuracy and scalability of computational methods for evolutionary relationship estimation.
  • To support the development of novel alignment and tree inference methods.

Main Methods:

  • Assembly of web pages containing benchmark datasets and software tools.
  • Inclusion of empirical datasets with curated alignments for testing alignment and phylogenetic methods.
  • Provision of simulated datasets mimicking large-scale systematics studies, with true alignments and trees.
  • Inclusion of links to external empirical datasets and software for data generation and evaluation.

Main Results:

  • A curated collection of benchmark datasets for multiple sequence alignment, phylogeny estimation, and supertree estimation.
  • Availability of simulated datasets with challenging properties like high substitution and indel rates.
  • Links to relevant software tools for data simulation and method evaluation.
  • A resource designed to address the needs of large-scale systematics studies.

Conclusions:

  • The developed resource provides a valuable platform for the scientific community to test and enhance computational methods in evolutionary biology.
  • It specifically aids in addressing challenges related to multiple sequence alignment and phylogeny estimation on difficult datasets.
  • The benchmark datasets and tools are intended to drive innovation in the development of more accurate and scalable bioinformatics tools.