Data from: A Poissonian model of indel rate variation for phylogenetic tree inference

While indel rate variation has been observed and analyzed in detail, it is not taken into account by current indel-aware phylogenetic reconstruction methods. In this work, we introduce a continuous time stochastic process, the geometric Poisson indel process, that generalizes the Poisson indel process by allowing insertion and deletion rates to vary across sites. We design an efficient algorithm for computing the probability of a given multiple sequence alignment based on our new indel model. We describe a method to construct phylogeny estimates from a fixed alignment using neighbor joining. Using simulation studies, we show that ignoring indel rate variation may have a detrimental effect on the accuracy of the inferred phylogenies, and that our proposed method can sidestep this issue by inferring latent indel rate categories. We also show that our phylogenetic inference method may be more stable to taxa subsampling in a real data experiment compared to some existing methods that either ignore indels or ignore indel rate variation, based on the weighted Robinson-Foulds distance that measures both topology similarity and branch length similarity of phylogenetic trees.尽管插入缺失（indel）速率变异已被细致观测与分析，但当前考虑插入缺失的系统发育重建方法均未将其纳入考量。本研究提出一种连续时间随机过程——几何泊松插入缺失过程（geometric Poisson indel process），该过程通过允许不同位点拥有差异化的插入与缺失速率，对泊松插入缺失过程进行了推广。我们设计了一种高效算法，可基于这一新的插入缺失模型计算给定多序列比对的概率。我们还提出了一种基于邻接法（neighbor joining）从固定多序列比对构建系统发育估计的方法。通过模拟实验，我们证明忽略插入缺失速率变异会对推断出的系统发育树的准确性产生不利影响，而我们所提出的方法可通过推断潜在插入缺失速率类别规避这一问题。此外，在一项真实数据实验中，基于同时衡量系统发育树拓扑结构相似度与分支长度相似度的加权罗宾逊-福尔兹距离（weighted Robinson-Foulds distance），相较于部分忽略插入缺失或忽略插入缺失速率变异的现有方法，我们的系统发育推断方法对类群抽样的稳定性更为优异。