Integrating Sequence Evolution into Probabilistic Orthology Analysis
2015 (English)In: Systematic Biology, ISSN 1063-5157, E-ISSN 1076-836X, Vol. 64, no 6, 969-982 p.Article in journal (Refereed) Published
Orthology analysis, that is, finding out whether a pair of homologous genes are orthologs - stemming from a speciation - or paralogs - stemming from a gene duplication - is of central importance in computational biology, genome annotation, and phylogenetic inference. In particular, an orthologous relationship makes functional equivalence of the two genes highly likely. A major approach to orthology analysis is to reconcile a gene tree to the corresponding species tree, (most commonly performed using the most parsimonious reconciliation, MPR). However, most such phylogenetic orthology methods infer the gene tree without considering the constraints implied by the species tree and, perhaps even more importantly, only allow the gene sequences to influence the orthology analysis through the a priori reconstructed gene tree. We propose a sound, comprehensive Bayesian MCMC-based method, DLRSOrthology, to compute orthology probabilities. It efficiently sums over the possible gene trees and jointly takes into account the current gene tree, all possible reconciliations to the species tree, and the, typically strong, signal conveyed by the sequences. We compare our method with PrIME-GEM, a probabilistic orthology approach built on a probabilistic duplication-loss model, and MrBayesMPR, a probabilistic orthology approach that is based on conventional Bayesian inference coupled with MPR. We find that DLRSOrthology outperforms these competing approaches on synthetic data as well as on biological data sets and is robust to incomplete taxon sampling artifacts.
Place, publisher, year, edition, pages
Oxford University Press, 2015. Vol. 64, no 6, 969-982 p.
Comparative genomics, orthology, paralogy, gene duplication, gene loss, sequence evolution, relaxed molecular clock, probabilistic modeling, phylogenetics, tree reconciliation, tree realization
Bioinformatics (Computational Biology)
Research subject Computer Science
IdentifiersURN: urn:nbn:se:kth:diva-168167DOI: 10.1093/sysbio/syv044ISI: 000363168100007ScopusID: 2-s2.0-84946111865OAI: oai:DiVA.org:kth-168167DiVA: diva2:814543
FunderSwedish e‐Science Research CenterSwedish Research Council, 2010-4757Magnus Bergvall Foundation
QC 20151216. Updated from manuscript to article in journal.2015-05-272015-05-272015-12-16Bibliographically approved