Phylogeny reconstruction: Increasing the accuracy of pairwise distance estimation using Bayesian inference of evolutionary rates

Matan Ninio, Eyal Privman, Tal Pupko, N. Friedman

Research output: Contribution to journalArticlepeer-review


Distance-based methods for phylogeny reconstruction are the fastest and easiest to use, and their popularity is accordingly high. They are also the only known methods that can cope with huge datasets of thousands of sequences. These methods rely on evolutionary distance estimation and are sensitive to errors in such estimations. In this study, a novel Bayesian method for estimation of evolutionary distances is developed. The proposed method enables the use of a sophisticated evolutionary model that better accounts for among-site rate variation (ASRV), thereby improving the accuracy of distance estimation. Rate variations are estimated within a Bayesian framework by extracting information from the entire dataset of sequences, unlike standard methods that can only use one pair of sequences at a time. We compare the accuracy of a cascade of distance estimation methods, starting from commonly used methods and moving towards the more sophisticated novel method. Simulation studies show significant improvements in the accuracy of distance estimation by the novel method over the commonly used ones. We demonstrate the effect of the improved accuracy on tree reconstruction using both real and simulated protein sequence alignments. An implementation of this method is available as part of the SEMPHY package.

Original languageEnglish
Pages (from-to)e136-e141
Issue number2
StatePublished - 2007
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported in part by an Israeli Science Foundation grant number 1208/04. TP was supported by a grant in Complexity Science from the Yeshaia Horvitz Association, and by a grant from the Israel Ministry of Science.

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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