A Bayesian Approach for Fast and Accurate Gene Tree Reconstruction
Name
Rasmussen-2010-A Bayesian Approach for Fast and Accurate Gene Tree.pdf
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735.82 KB
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Adobe PDF
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Author(s)
Rasmussen, Matthew D.
Kellis, Manolis
Date Issued
July 2010
Journal
Molecular Biology and Evolution
Publisher
Oxford University Press (OUP)
Citation
Rasmussen, M. D., and M. Kellis. “A Bayesian Approach for Fast and Accurate Gene Tree Reconstruction.” Molecular Biology and Evolution 28.1 (2010): 273–290. Web. 25 May 2012.
Version
Final published version
Abstract
Recent sequencing and computing advances have enabled phylogenetic analyses to expand to both entire genomes and large clades, thus requiring more efficient and accurate methods designed specifically for the phylogenomic context. Here, we present SPIMAP, an efficient Bayesian method for reconstructing gene trees in the presence of a known species tree. We observe many improvements in reconstruction accuracy, achieved by modeling multiple aspects of evolution, including gene duplication and loss (DL) rates, speciation times, and correlated substitution rate variation across both species and loci. We have implemented and applied this method on two clades of fully sequenced species, 12 Drosophila and 16 fungal genomes as well as simulated phylogenies and find dramatic improvements in reconstruction accuracy as compared with the most popular existing methods, including those that take the species tree into account. We find that reconstruction inaccuracies of traditional phylogenetic methods overestimate the number of DL events by as much as 2–3-fold, whereas our method achieves significantly higher accuracy. We feel that the results and methods presented here will have many important implications for future investigations of gene evolution.
Description
Supplementary tables S1, sections 2.1–2.3, and figures S1–S11 are available at Molecular Biology and Evolution online (http://www.mbe.oxfordjournals.org/).
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution Non-Commercial
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DOI of Published Version
https://doi.org/10.1093/molbev/msq189
