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Probabilistic inference of lateral gene transfer events
Stockholms universitet, Naturvetenskapliga fakulteten, Numerisk analys och datalogi (NADA). Stockholms universitet, Science for Life Laboratory (SciLifeLab). Swedish e-Science Research Centre, Sweden.ORCID-id: 0000-0001-5341-1733
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Antal upphovsmän: 52016 (Engelska)Ingår i: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 17, nr Suppl 14, artikel-id 431Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Background: Lateral gene transfer (LGT) is an evolutionary process that has an important role in biology. It challenges the traditional binary tree-like evolution of species and is attracting increasing attention of the molecular biologists due to its involvement in antibiotic resistance. A number of attempts have been made to model LGT in the presence of gene duplication and loss, but reliably placing LGT events in the species tree has remained a challenge.

Results: In this paper, we propose probabilistic methods that samples reconciliations of the gene tree with a dated species tree and computes maximum a posteriori probabilities. The MCMC-based method uses the probabilistic model DLTRS, that integrates LGT, gene duplication, gene loss, and sequence evolution under a relaxed molecular clock for substitution rates. We can estimate posterior distributions on gene trees and, in contrast to previous work, the actual placement of potential LGT, which can be used to, e.g., identify highways of LGT.

Conclusions: Based on a simulation study, we conclude that the method is able to infer the true LGT events on gene tree and reconcile it to the correct edges on the species tree in most cases. Applied to two biological datasets, containing gene families from Cyanobacteria and Molicutes, we find potential LGTs highways that corroborate other studies as well as previously undetected examples.

Ort, förlag, år, upplaga, sidor
2016. Vol. 17, nr Suppl 14, artikel-id 431
Nyckelord [en]
Evolution, Bayesian inference, Phylogeny, Lateral gene transfer
Nationell ämneskategori
Biologiska vetenskaper Miljöbioteknik
Identifikatorer
URN: urn:nbn:se:su:diva-140273DOI: 10.1186/s12859-016-1268-2ISI: 000392515100009PubMedID: 28185583OAI: oai:DiVA.org:su-140273DiVA, id: diva2:1083885
Konferens
14th Annual Research in Computational Molecular Biology (RECOMB), Montreal, Canada, 11-14 October 2016
Tillgänglig från: 2017-03-22 Skapad: 2017-03-22 Senast uppdaterad: 2020-03-04Bibliografiskt granskad

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Arvestad, Lars
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Numerisk analys och datalogi (NADA)Science for Life Laboratory (SciLifeLab)
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