Format

Send to

Choose Destination
Syst Biol. 2014 May;63(3):409-20. doi: 10.1093/sysbio/syu007. Epub 2014 Feb 20.

A Bayesian method for analyzing lateral gene transfer.

Author information

1
Science 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.

Abstract

Lateral gene transfer (LGT)--which transfers DNA between two non-vertically related individuals belonging to the same or different species--is recognized as a major force in prokaryotic evolution, and evidence of its impact on eukaryotic evolution is ever increasing. LGT has attracted much public attention for its potential to transfer pathogenic elements and antibiotic resistance in bacteria, and to transfer pesticide resistance from genetically modified crops to other plants. In a wider perspective, there is a growing body of studies highlighting the role of LGT in enabling organisms to occupy new niches or adapt to environmental changes. The challenge LGT poses to the standard tree-based conception of evolution is also being debated. Studies of LGT have, however, been severely limited by a lack of computational tools. The best currently available LGT algorithms are parsimony-based phylogenetic methods, which require a pre-computed gene tree and cannot choose between sometimes wildly differing most parsimonious solutions. Moreover, in many studies, simple heuristics are applied that can only handle putative orthologs and completely disregard gene duplications (GDs). Consequently, proposed LGT among specific gene families, and the rate of LGT in general, remain debated. We present a Bayesian Markov-chain Monte Carlo-based method that integrates GD, gene loss, LGT, and sequence evolution, and apply the method in a genome-wide analysis of two groups of bacteria: Mollicutes and Cyanobacteria. Our analyses show that although the LGT rate between distant species is high, the net combined rate of duplication and close-species LGT is on average higher. We also show that the common practice of disregarding reconcilability in gene tree inference overestimates the number of LGT and duplication events.

PMID:
24562812
DOI:
10.1093/sysbio/syu007
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Silverchair Information Systems
Loading ...
Support Center