Evolution and Phylogenetics
Phylogeny reconstruction: increasing the accuracy of pairwise distance estimation using Bayesian inference of evolutionary rates
1 The Selim and Rachel Benin School of Computer Science and Engineering, Hebrew University Jerusalem 91904, Israel
2 Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences Tel Aviv University, Tel Aviv 69978, Israel
*To whom correspondence should be addressed.
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Abstract |
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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.
Contact: talp{at}tau.ac.il
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.
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