Data from: A Bayesian method for analyzing lateral gene transfer

Lateral gene transfer (LGT)—which transfers DNA between two non-vertically related individuals belonging to the same or different species—is recognized as a major force in prokaryotic evolution, and evidence of its impact on eukaryotic evolution is ever increasing. LGT has attracted much public attention for its potential to transfer pathogenic elements and antibiotic resistance in bacteria, and to transfer pesticide resistance from genetically modified crops to other plants. In a wider perspective, there is a growing body of studies highlighting the role of LGT in enabling organisms to occupy new niches or adapt to environmental changes. The challenge LGT poses to the standard tree-based conception of evolution is also being debated. Studies of LGT have, however, been severely limited by a lack of computational tools. The best currently available LGT algorithms are parsimony-based phylogenetic methods, which require a pre-computed gene tree and cannot choose between sometimes wildly differing most-parsimonious solutions. Moreover, in many studies, simple heuristics are applied that can only handle putative orthologs and completely disregard gene duplications. Consequently, proposed LGT among specific gene families, and the rate of LGT in general remain debated. We present a Bayesian MCMC-based method that integrates gene duplication, gene loss, LGT, and sequence evolution, and apply the method in a genome-wide analysis of two groups of bacteria: Mollicutes and Cyanobacteria. Our analyses show that although the LGT rate between distant species is high, the net combined rate of duplication and close species-LGT is on average higher. We also show that the common practice of disregarding reconcilability in gene tree inference overestimates the number of LGT and duplication events.横向基因转移（Lateral gene transfer，LGT）指在同物种或不同物种的两个非垂直亲缘关系个体之间传递DNA，该过程被认为是原核生物演化的主要驱动力之一，而其对真核生物演化产生影响的证据也日益增多。LGT因具备在细菌间传递致病因子与抗生素抗性、从转基因作物向其他植物传递抗药性的潜力，受到了广泛关注。从更宏观的视角来看，越来越多的研究表明，LGT在帮助生物占据新生态位或适应环境变化方面发挥着关键作用。LGT对传统的基于系统发育树的演化观提出的挑战，也一直是学界热议的话题。然而，由于缺乏合适的计算工具，相关LGT研究长期以来受到严重制约。当前可用的最优LGT算法多为基于简约性的系统发育方法，这类方法需要预先构建基因树，且无法在有时差异极大的最简约解之间做出选择。此外，许多研究中采用的简单启发式方法仅能处理推定的直系同源物，完全忽略了基因重复事件。因此，针对特定基因家族间存在LGT的论断，以及整体LGT发生速率的相关结论，至今仍存在争议。本研究提出了一种整合了基因重复、基因丢失、LGT与序列演化的基于贝叶斯马尔可夫链蒙特卡洛（Bayesian Markov Chain Monte Carlo，MCMC）的方法，并将该方法应用于柔膜菌纲（Mollicutes）与蓝细菌门（Cyanobacteria）两类细菌的全基因组分析。分析结果表明，尽管远缘物种间的LGT速率较高，但基因重复与近缘物种间LGT的净综合速率平均而言更高。本研究同时证实，在基因树推断过程中忽略可协调性检验的常规做法，会高估LGT与基因重复事件的发生数量。