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MBE Advance Access originally published online on May 14, 2009
Molecular Biology and Evolution 2009 26(9):1931-1939; doi:10.1093/molbev/msp105
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© The Author 2009. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org

Research Articles

A Machine-Learning Approach Reveals That Alignment Properties Alone Can Accurately Predict Inference of Lateral Gene Transfer from Discordant Phylogenies

Mayo Roettger, William Martin and Tal Dagan

Institut für Botanik III, Heinrich-Heine Universität Düsseldorf, Germany

E-mail: mayo.roettger{at}uni-duesseldorf.de.

Accepted for publication May 5, 2009.

Among the methods currently used in phylogenomic practice to detect the presence of lateral gene transfer (LGT), one of the most frequently employed is the comparison of gene tree topologies for different genes. In cases where the phylogenies for different genes are incompatible, or discordant, for well-supported branches there are three simple interpretations for the result: 1) gene duplications (paralogy) followed by many independent gene losses have occurred, 2) LGT has occurred, or 3) the phylogeny is well supported but for reasons unknown is nonetheless incorrect. Here, we focus on the third possibility by examining the properties of 22,437 published multiple sequence alignments, the Bayesian maximum likelihood trees for which either do or do not suggest the occurrence of LGT by the criterion of discordant branches. The alignments that produce discordant phylogenies differ significantly in several salient alignment properties from those that do not. Using a support vector machine, we were able to predict the inference of discordant tree topologies with up to 80% accuracy from alignment properties alone.

Key Words: lateral gene transfer • molecular phylogeny • discordant tree topologies • support vector machine • principal component analysis

Dan Graur, Associate Editor


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[Abstract] [Full Text] [PDF]


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