MONI: A Pangenomic Index for Finding Maximal Exact Matches
MONI: A Pangenomic Index for Finding Maximal Exact Matches
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DOI：
10.1089/cmb.2021.0290
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发表时间：
2022-01-17
影响因子：
1.7
通讯作者：
Boucher, Christina
中科院分区：
文献类型：
--
作者：
Rossi, Massimiliano;Oliva, Marco;Boucher, Christina
最近，Gagie等人。提出了一种名为r-index的fm索引版本，它可以在一台商用计算机上存储数千个人类基因组。然后是Kuhnle等人。展示了如何通过一种称为无前缀解析(PFP)的技术高效地构建r-索引，并演示了其在精确模式匹配方面的有效性。精确模式匹配可以被用来支持近似模式匹配，但是r-index本身不能有效地支持流行和重要的查询，例如查找最大精确匹配(MEMS)。为了解决这一缺陷，Bannai等人。引入了阈值的概念，并表明将它们与r指数存储在一起可以有效地找到MEM-但他们没有说明如何找到这些阈值。我们提出了一种新的算法，应用PFP来建立r-索引，并根据无前缀句法分析的大小在线性的时间和空间内同时寻找阈值。我们的实现名为MONI，可以在高重复序列的读数和大序列集合之间快速找到MEMS。与其他读对齐器--PuffAligner、Bowtie2、BWA-MEM和Chic-Moni相比，Moni使用的内存少2-11倍，构建索引的速度快2-32倍。此外，MONI的大小不到人类大染色体集合竞争索引的千分之一。因此，MONI代表着我们在针对非常大的相关参考集合执行MEM查找的能力方面的重大进步。
Recently, Gagie et al. proposed a version of the FM-index, called the r-index, that can store thousands of human genomes on a commodity computer. Then Kuhnle et al. showed how to build the r-index efficiently via a technique called prefix-free parsing (PFP) and demonstrated its effectiveness for exact pattern matching. Exact pattern matching can be leveraged to support approximate pattern matching, but the r-index itself cannot support efficiently popular and important queries such as finding maximal exact matches (MEMs). To address this shortcoming, Bannai et al. introduced the concept of thresholds, and showed that storing them together with the r-index enables efficient MEM finding-but they did not say how to find those thresholds. We present a novel algorithm that applies PFP to build the r-index and find the thresholds simultaneously and in linear time and space with respect to the size of the prefix-free parse. Our implementation called MONI can rapidly find MEMs between reads and large-sequence collections of highly repetitive sequences. Compared with other read aligners-PuffAligner, Bowtie2, BWA-MEM, and CHIC- MONI used 2-11 times less memory and was 2-32 times faster for index construction. Moreover, MONI was less than one thousandth the size of competing indexes for large collections of human chromosomes. Thus, MONI represents a major advance in our ability to perform MEM finding against very large collections of related references.