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Probabilistic inference of lateral gene transfer events
KTH, School of Computer Science and Communication (CSC). KTH, Centres, Science for Life Laboratory, SciLifeLab.
KTH, School of Computer Science and Communication (CSC). KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-8034-7834
KTH, School of Computer Science and Communication (CSC). KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-2791-8773
KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, Centres, SeRC - Swedish e-Science Research Centre. Stockholm Univ, Sweden.ORCID iD: 0000-0001-5341-1733
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2016 (English)In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 17, 431Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
BioMed Central, 2016. Vol. 17, 431
Keyword [en]
Evolution, Bayesian inference, Phylogeny, Lateral gene transfer
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:kth:diva-202455DOI: 10.1186/s12859-016-1268-2ISI: 000392515100009Scopus ID: 2-s2.0-85003029567OAI: oai:DiVA.org:kth-202455DiVA: diva2:1078379
Conference
14th Annual Research in Computational Molecular Biology (RECOMB) Comparative Genomics Satellite Workshop - Bioinformatics, OCT 11-14, 2016, Montreal, CANADA
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceSwedish e‐Science Research CenterSwedish National Infrastructure for Computing (SNIC), snic2014-3-5
Note

QC 20170303

Available from: 2017-03-03 Created: 2017-03-03 Last updated: 2017-03-03Bibliographically approved

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Khan, Mehmood AlamMahmudi, OwaisUlah, IkramArvestad, LarsLagergren, Jens
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School of Computer Science and Communication (CSC)Science for Life Laboratory, SciLifeLabSeRC - Swedish e-Science Research CentreComputational Science and Technology (CST)
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BMC Bioinformatics
Bioinformatics (Computational Biology)

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