Improved gene tree error correction in the presence of horizontal gene transfer
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Bansal-2014-Improved gene tree.pdf
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Author(s)
Bansal, Mukul S.
Wu, Yi-Chieh
Alm, Eric J.
Kellis, Manolis
Date Issued
December 2014
Journal
Bioinformatics
Publisher
Oxford University Press
Citation
Bansal, M. S., Y.-C. Wu, E. J. Alm, and M. Kellis. “Improved Gene Tree Error Correction in the Presence of Horizontal Gene Transfer.” Bioinformatics 31, no. 8 (December 5, 2014): 1211–1218.
Version
Final published version
Abstract
Motivation: The accurate inference of gene trees is a necessary step in many evolutionary studies. Although the problem of accurate gene tree inference has received considerable attention, most existing methods are only applicable to gene families unaffected by horizontal gene transfer. As a result, the accurate inference of gene trees affected by horizontal gene transfer remains a largely unaddressed problem.
Results: In this study, we introduce a new and highly effective method for gene tree error correction in the presence of horizontal gene transfer. Our method efficiently models horizontal gene transfers, gene duplications and losses, and uses a statistical hypothesis testing framework [Shimodaira–Hasegawa (SH) test] to balance sequence likelihood with topological information from a known species tree. Using a thorough simulation study, we show that existing phylogenetic methods yield inaccurate gene trees when applied to horizontally transferred gene families and that our method dramatically improves gene tree accuracy. We apply our method to a dataset of 11 cyanobacterial species and demonstrate the large impact of gene tree accuracy on downstream evolutionary analyses.
Availability and implementation: An implementation of our method is available at http://compbio.mit.edu/treefix-dtl/
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1093/bioinformatics/btu806
