Data from: Integrating sequence evolution into probabilistic orthology analysis

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.直系同源分析（Orthology analysis）指判断一对同源基因（homologous genes）究竟是源于物种形成（speciation）的直系同源基因（orthologs），还是源于基因重复（gene duplication）的旁系同源基因（paralogs），其在计算生物学（computational biology）、基因组注释（genome annotation）与系统发育推断（phylogenetic inference）领域均具有核心重要性。 尤为关键的是，直系同源关系可高度预示两个基因具备功能等价性（functional equivalence）。当前主流的直系同源分析思路，是将基因树（gene tree）与对应物种树（species tree）进行共祖校准，其中最常用的实现方式为最简约共祖分析（most parsimonious reconciliation, MPR）。 然而，绝大多数此类系统发育直系同源分析方法在推断基因树时，并未考虑物种树所蕴含的约束条件；更重要的是，这类方法仅能通过预先重构的基因树，使基因序列对直系同源分析产生影响。本研究提出了一种严谨且全面的基于贝叶斯马尔可夫链蒙特卡洛（Bayesian MCMC）的方法DLRSOrthology，用于计算直系同源概率。该方法可高效枚举所有可能的基因树，同时联合考量当前基因树、所有与物种树匹配的共祖场景，以及序列所传递的通常具有较强信息量的系统发育信号。 我们将本方法与两款现有对比方法进行了评估：一款是基于概率复制-丢失模型（probabilistic duplication-loss model）构建的概率直系同源分析方法PrIME-GEM，另一款是结合常规贝叶斯推断（conventional Bayesian inference）与MPR的概率直系同源分析方法MrBayesMPR。实验结果显示，DLRSOrthology在模拟数据（synthetic data）与生物数据集（biological data sets）上均优于上述两款对比方法，且对类群取样不全伪影（incomplete taxon sampling artifacts）具有良好的鲁棒性。