基于弹性简并字符串的泛基因组优化
Pangenome Optimization via Elastic Degenerate Strings
- University of Helsinki（赫尔辛基大学）
- University of Pisa（比萨大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出将创始人重建方法适配到弹性简并字符串（EDS）上，在线性时间内最小化泛基因组的基数或总大小，并实现工具mincard进行首次可扩展优化实验。
AI中文摘要：
弹性简并字符串（EDS，或ED-string）是字符串集合的序列。泛基因组，由种群中沿基因组序列观察到的变异组成，可以自然地编码为EDS。文献中已广泛研究了诸如EDS之类的泛基因组表示上的模式匹配和比较问题，但在构建过程中优化泛基因组属性在很大程度上被忽略了。我们通过展示如何将最初为创始人重建相关问题开发的方法进行调整，以在线性时间内最小化EDS集合的总基数或EDS字符串的总大小（给定表示输入数据的合适多重比对），来填补这一空白。我们提供了一个实现最小基数准则的工具mincard，并进行了首次基于EDS的可扩展泛基因组优化实验。代码和实验可在以下网址获取：此https URL。
英文摘要：
An Elastic Degenerate String (EDS, or ED-string) is a sequence of string sets. A pangenome, consisting of variations observed in a population along the genome sequences, can be naturally encoded as an EDS. Pattern matching and comparison problems on pangenome representations such as EDSes have been widely studied in the literature, but optimizing the pangenome properties during its construction has been largely omitted. We fill this gap by showing how methods originally developed for the related problem of founder reconstruction can be adapted to minimize, in linear time, the total cardinality of the EDS sets or the total size of the EDS strings, given suitable multiple alignments representing the input data. We provide an implementation for the minimum-cardinality criterion in a tool mincard, and conduct the first experiments on scalable pangenome optimization via EDSes. The code and experiments are available at https://github.com/algbio/eds.
