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A Bayesian method for analyzing lateral gene transfer.

Joel Sjöstrand1, Ali Tofigh, Vincent Daubin

  • 1Science for Life Laboratory, Tomtebodavägen 23A, 17165 Solna, Sweden, Department of Numerical Analysis and Computer Science, Stockholm University, Sweden, McGill Centre for Bioinformatics, 4th floor, Bellini Building, Life Sciences Complex, 3649 Promenade Sir William Osler, Montreal, Quebec, Canada, H3G 0B1, UMR CNRS 5558 - LBBE, "Biométrie et Biologie évolutive", UCB Lyon 1 - Bât. Grégor Mendel, 43 bd du 11 novembre 1918, 69622 VILLEURBANNE cedex, Department of Mechanics, Osquars Backe 18, KTH, SE-100 44 Stockholm, Sweden, Karolinska University Hospital, CMM L8:03, Solna, SE-171 76 Stockholm, Sweden and The School of Computer Science and Communication, Lindstedtsvägen 3, 5, KTH CSC, SE-100 44 Stockholm, Sweden.

Systematic Biology
|February 25, 2014
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Summary
This summary is machine-generated.

Lateral gene transfer (LGT) significantly impacts evolution, but current methods often overestimate its rate. A new Bayesian approach accurately models LGT, gene duplication, and loss, revealing a higher net rate for duplication and close-species LGT.

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Area of Science:

  • Evolutionary biology
  • Genomics
  • Computational biology

Background:

  • Lateral gene transfer (LGT) is a key evolutionary mechanism in prokaryotes and increasingly recognized in eukaryotes.
  • LGT has implications for antibiotic resistance, pesticide resistance, and adaptation to new environments.
  • Existing computational tools for LGT analysis are limited, often disregarding gene duplication and loss.

Purpose of the Study:

  • To develop a novel computational method for analyzing LGT, gene duplication (GD), and gene loss.
  • To apply this method to a genome-wide analysis of Mollicutes and Cyanobacteria.
  • To reassess the rate and impact of LGT in bacterial evolution.

Main Methods:

  • Developed a Bayesian Markov-chain Monte Carlo (MCMC)-based method.
  • Integrated gene duplication, gene loss, LGT, and sequence evolution into a single model.
  • Performed genome-wide analysis on Mollicutes and Cyanobacteria.

Main Results:

  • The new method accurately models LGT, GD, and gene loss.
  • Genome-wide analyses in Mollicutes and Cyanobacteria were performed.
  • Although LGT between distant species is high, the net combined rate of duplication and close-species LGT is higher on average.
  • Disregarding reconcilability in gene tree inference overestimates LGT and duplication events.

Conclusions:

  • The developed Bayesian MCMC method provides a more accurate framework for studying LGT and gene duplication.
  • The study reveals that gene duplication and close-species LGT are significant factors in bacterial evolution.
  • Current practices in gene tree inference may lead to overestimations of evolutionary events like LGT.