Recovering the treelike trend of evolution despite extensive lateral genetic transfer: A probabilistic analysis

Sebastien Roch, Sagi Snir

Research output: Contribution to journalArticlepeer-review

Abstract

Lateral gene transfer (LGT) is a common mechanism of nonvertical evolution, during which genetic material is transferred between two more or less distantly related organisms. It is particularly common in bacteria where it contributes to adaptive evolution with important medical implications. In evolutionary studies, LGT has been shown to create widespread discordance between gene trees as genomes become mosaics of gene histories. In particular, the Tree of Life has been questioned as an appropriate representation of bacterial evolutionary history. Nevertheless a common hypothesis is that prokaryotic evolution is primarily treelike, but that the underlying trend is obscured by LGT. Extensive empirical work has sought to extract a common treelike signal from conflicting gene trees. Here we give a probabilistic perspective on the problem of recovering the treelike trend despite LGT. Under a model of randomly distributed LGT, we show that the species phylogeny can be reconstructed even in the presence of surprisingly many (almost linear number of) LGT events per gene tree. Our results, which are optimal up to logarithmic factors, are based on the analysis of a robust, computationally efficient reconstruction method and provides insight into the design of such methods. Finally, we show that our results have implications for the discovery of highways of gene sharing.

Original languageEnglish
Pages (from-to)93-112
Number of pages20
JournalJournal of Computational Biology
Volume20
Issue number2
DOIs
StatePublished - 1 Feb 2013

Keywords

  • lateral gene transfer
  • phylogenetic reconstruction
  • quartet reconstruction

ASJC Scopus subject areas

  • Computational Mathematics
  • Genetics
  • Molecular Biology
  • Computational Theory and Mathematics
  • Modeling and Simulation

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  • Recovering the tree-like trend of evolution despite extensive lateral genetic transfer: A probabilistic analysis

    Roch, S. & Snir, S., 2012, Research in Computational Molecular Biology - 16th Annual International Conference, RECOMB 2012, Proceedings. p. 224-238 15 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 7262 LNBI).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Open Access

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