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A practical exact maximum compatibility algorithm for reconstruction of recent evolutionary history.

Joshua L Cherry1

  • 1National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA. jcherry@ncbi.nlm.nih.gov.

BMC Bioinformatics
|February 25, 2017
PubMed
Summary

Maximum compatibility, a phylogenetic method, is now efficient for analyzing bacterial whole-genome sequences. This new algorithm rapidly reconstructs bacterial phylogenies, proving robust against misleading data.

Keywords:
Bacterial genomesHomoplasyMaximum compatibilityPhylogeny

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Maximum compatibility is an underutilized phylogenetic reconstruction method, particularly for molecular sequences.
  • It shows potential for specific applications like bacterial phylogeny reconstruction using whole-genome sequencing data.

Purpose of the Study:

  • To present a novel algorithm for rapidly computing phylogenies based on the maximum compatibility criterion.
  • To address the challenges of applying maximum compatibility to large molecular datasets, especially bacterial genomes.

Main Methods:

  • Development of an algorithm that efficiently solves the maximum compatibility problem, adapted for phylogenetic reconstruction.
  • The algorithm handles data ambiguities and is based on solutions to the maximum clique problem.
  • Application to bacterial datasets with up to 2000 genomes and thousands of variable nucleotide sites.

Main Results:

  • The algorithm achieves rapid computation times, typically seconds or less, for large bacterial datasets.
  • Demonstrated that maximum compatibility is more resilient than maximum parsimony to misleading nucleotide data.
  • Successfully applied to reconstruct phylogenies for closely-related bacteria using whole-genome sequencing data.

Conclusions:

  • Maximum compatibility is a valuable tool for specific phylogenetic challenges, notably inferring relationships in closely-related bacteria from whole-genome data.
  • The presented algorithm offers a fast and robust solution for large-scale phylogenetic problems involving bacterial genomes.
  • The method provides enhanced reliability by mitigating the impact of misleading characters common in large sequencing datasets.