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 than methods that either ignore indels or indel rate variation.

尽管插入缺失（indel）速率变异已被广泛观察并得到细致分析，但当前考虑插入缺失的系统发育重建（phylogenetic reconstruction）方法均未将其纳入考量范畴。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），该模型通过允许不同位点拥有差异化的插入与缺失速率，对传统泊松插入缺失过程进行了推广。我们设计了一种高效算法，可基于该新型插入缺失模型计算给定多序列比对（multiple sequence alignment）的概率。我们还提出了一种基于固定序列比对、通过邻接法（neighbor joining）构建系统发育估计结果的方法。通过模拟实验，我们证实：忽略插入缺失速率变异会对推断得到的系统发育树的准确性产生显著不利影响，而我们提出的方法可通过推断潜在插入缺失速率类别规避该问题。此外，我们还发现，相较于忽略插入缺失或插入缺失速率变异的方法，本研究的系统发育推断方法在类群采样（taxa subsampling）场景下具备更优的稳定性。