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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Computing the Joint Distribution of Tree Shape and Tree Distance for Gene Tree Inference and Recombination Detection
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Computing the Joint Distribution of Tree Shape and Tree Distance for Gene Tree Inference and Recombination Detection

机译：计算树形和树距离的联合分布以进行基因树推断和重组检测

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摘要

Ancestral recombination events can cause the underlying genealogy of a site to vary along the genome. We consider Bayesian models to simultaneously detect recombination breakpoints in very long sequence alignments and estimate the phylogenetic tree of each block between breakpoints. The models we consider use a dissimilarity measure between trees in their prior distribution to favor similar trees at neighboring loci. We show empirical evidence in Enterobacteria that neighboring genomic regions have similar trees. The main hurdle to using such models is the need to properly calculate the normalizing function for the prior probabilities on trees. In this work, we quantify the impact of approximating this normalizing function as done in biomc2, a hierarchical Bayesian method to detect recombination based on distance between tree topologies. We then derive an algorithm to calculate the normalizing function exactly, for a Gibbs distribution based on the Robinson-Foulds (RF) distance between gene trees at neighboring loci. At the core is the calculation of the joint distribution of the shape of a random tree and its RF distance to a fixed tree. We also propose fast approximations to the normalizing function, which are shown to be very accurate with little impact on the Bayesian inference.
机译：祖先的重组事件可能会导致位点的潜在族谱沿基因组变化。我们认为贝叶斯模型可以在很长的序列比对中同时检测重组断裂点，并估计断裂点之间每个模块的系统发育树。我们考虑的模型在树的先验分布中使用树之间的相异性度量来支持相邻基因座处的相似树。我们在肠杆菌中显示经验证据，证明邻近的基因组区域具有相似的树木。使用这种模型的主要障碍是需要正确计算树上先验概率的归一化函数。在这项工作中，我们将量化归一化函数的影响，如biomc2中所做的那样，biomc2是一种基于树形拓扑之间的距离来检测重组的分层贝叶斯方法。然后，我们根据相邻位置基因树之间的Robinson-Foulds（RF）距离，为Gibbs分布推导一种算法，以精确计算归一化函数。核心是计算随机树的形状及其与固定树的RF距离的联合分布。我们还建议对归一化函数进行快速逼近，该逼近函数非常精确，对贝叶斯推断的影响很小。

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