Data from: Integrating sequence evolution into probabilistic orthology analysis

• Main Authors: Ullah, Ikram, Sjöstrand, Joel, Andersson, Peter, Sennblad, Bengt, Lagergren, Jens
• Format: Dataset
• Language: unknown
• Published: Dryad Digital Repository 2015
• Subjects: Comparative genomics; Phylogenetics; Orthology; Paralogy; Sequence Evolution; Gene Duplication; Gene Loss; Probabilistic Modeling; Molecular Evolution; Relaxed Molecular Clock; Tree Reconciliation; Tree Realization; Homo sapiens; Mus musculus; Gallus gallus; Taeniopygia guttata; Canis lupus familiaris; Monodelphis domestica; Ciona intestinalis; Danio rerio; Ornithorhynchus anatinus; Life sciences; medicine and health care; stat; envir; Canis lupus
• Online Access: https://doi.org/10.5061/dryad.4r910

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 Markov chain Monte Carlo-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. Supplementary Material for “Integrating Sequence Evolution into Probabilistic Orthology Analysis”This document contains the supplementary information including synthetic data parameters, figures, and tables for the paper “Integrating Sequence Evolution into Probabilistic Orthology Analysis”Supplementary_Material.pdf