School of Plant Sciences and Food Security, Tel Aviv University, Tel Aviv, Israel.
Methods Mol Biol. 2023;2545:175-187. doi: 10.1007/978-1-0716-2561-3_9.
Chromosome numbers have long been used for the identification of key genomic events such as polyploidy and dysploidy. These inferences are often challenging, particularly when applied to large phylogenies, or clades in which more than a few chromosome number transitions had occurred. Here we describe the chromEvol computational framework that infers shifts in chromosome numbers along a phylogeny using probabilistic models of chromosome number change. Given chromosome count data and an associated phylogeny, chromEvol identifies such patterns by fitting probabilistic models of chromosome number evolution to the data. We describe the chromEvol workflow using available online tools, including the specification of the desired models, the examination of model fit to the data, and the inference of ploidy levels. The pipeline can be used by the wide scientific community and requires no previous computational or programming skills.
染色体数目长期以来一直被用于鉴定基因组关键事件，如多倍体和非整倍体。这些推断往往具有挑战性，特别是在应用于大型系统发育树或经历过多次染色体数目转变的类群时。在这里，我们描述了 chromEvol 计算框架，该框架使用染色体数变化的概率模型，沿系统发育推断染色体数的变化。给定染色体计数数据和相关的系统发育树，chromEvol 通过将染色体数演化的概率模型拟合到数据中来识别这些模式。我们使用可用的在线工具描述 chromEvol 工作流程，包括所需模型的指定、模型对数据的拟合程度以及倍性水平的推断。该管道可供广泛的科学界使用，不需要任何先前的计算或编程技能。
