KwARG：具有递归突变的祖先重组图的简约重构
KwARG: Parsimonious reconstruction of ancestral recombination graphs with recurrent mutation
摘要：
在存在重组和复发突变的情况下，给定遗传数据样本的可能历史的重建是一个具有挑战性的问题，但可以提供对种群进化的关键见解。我们提出了KwARG，它实现了一个基于吝啬的贪婪的启发式算法，寻找似是而非的系谱历史（祖先重组图）是最小或接近最小的假设重组和突变事件的数量。给定比对序列的输入数据集，KwARG输出可能的候选解决方案的列表，每个候选解决方案包括可能产生数据集的突变和重组事件的列表;解决方案中重组和复发突变的相对比例可以通过指定一组“成本”参数来控制。我们证明，该算法表现良好，与现有的方法相比。该软件在GitHub上提供。
英文摘要：
The reconstruction of possible histories given a sample of genetic data in the presence of recombination and recurrent mutation is a challenging problem, but can provide key insights into the evolution of a population. We present KwARG, which implements a parsimony-based greedy heuristic algorithm for finding plausible genealogical histories (ancestral recombination graphs) that are minimal or near-minimal in the number of posited recombination and mutation events. Given an input dataset of aligned sequences, KwARG outputs a list of possible candidate solutions, each comprising a list of mutation and recombination events that could have generated the dataset; the relative proportion of recombinations and recurrent mutations in a solution can be controlled via specifying a set of 'cost' parameters. We demonstrate that the algorithm performs well when compared against existing methods. The software is made available on GitHub.
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