Maximal exact matches (MEMs) have been widely used in bioinformatics at least since Li (2013) presented BWA-MEM. Building on work by Bannai, Gagie and I (2018), Rossi et al.\ (2022) recently built an index called MONI, based on the run-length compressed Burrows-Wheeler Transform, that can find MEMs efficiently with respect to pangenomes. In this paper we define $k$-MEMs to be maximal substrings of a pattern that each occur exactly at $k$ times in a text (so a MEM is a 1-MEM) and show that, when $k$ is given at construction time, MONI can find $k$-MEMs efficiently as well.
翻译:至少在自李(2013年)介绍BWA-MEM以来,生物信息学中广泛使用了最大精确匹配(MEMs)。根据Bannai、Gagie和I的工作(2018年),Rossi等人(2022年)最近在Bannai、Gagie和I的工作(2018年)的基础上,根据运行时间长度压缩的Burrows-Wheeler变形,最近建立了一个称为MONI的指数,该指数能够有效地发现MEMs在整形器方面的效率。在本文中,我们定义了$k$-MEMs是一种模式的最大分字符,每种模式在文本中都完全以k美元为单位(MEM是1-MEM),并表明,当施工时间提供K美元时,MONI也可以有效地找到k$-MEMs。