AMAS：优化自适应种子的划分和过滤，以加快读取映射
AMAS: optimizing the partition and filtration of adaptive seeds to speed up read mapping
摘要：
背景资料：识别下一代测序（NGS）读数的所有可能的映射位置在几个应用中是非常重要的，例如预测位于重复区域中的基因组变体或蛋白质结合基序，异构体表达定量，宏基因组学分析等。然而，这项任务非常耗时，大多数映射工具只关注一个或几个最佳映射位置。结果：我们提出了AMAS，一种专门用于识别参考序列中NGS读段的所有可能映射位置的比对工具。AMAS的特点是有效地使用自适应种子来加速读段映射，同时保持灵敏度。具体地，索引被设计为预存储自适应种子在参考序列中的位置，有效地减少了种子匹配和分割的时间。自适应种子的精确过滤被进一步应用以基本上收紧候选对准空间。因此，AMAS的运行速度比其他最先进的读取映射器快几倍，同时达到类似的精度。结论：AMAS提供了一个宝贵的资源，以加快重要而耗时的任务，确定所有映射位置的NGS读取。AMAS是基于SeqAn库用C++实现的，可以在https://sourceforge.net/projects/ngsamas/上免费获得。关键词：下一代测序，读段映射，序列比对，自适应种子，种子划分，过滤
英文摘要：
Background: Identifying all possible mapping locations of next-generation sequencing (NGS) reads is highly essential in several applications such as prediction of genomic variants or protein binding motifs located in repeat regions, isoform expression quantification, metagenomics analysis, etc. However, this task is very time-consuming and majority of mapping tools only focus on one or a few best mapping locations. Results: We propose AMAS, an alignment tool specialized in identifying all possible mapping locations of NGS reads in a reference sequence. AMAS features an effective use of adaptive seeds to speed up read mapping while preserving sensitivity. Specifically, an index is designed to pre-store the locations of adaptive seeds in the reference sequence, efficiently reducing the time for seed matching and partitioning. An accurate filtration of adaptive seeds is further applied to substantially tighten the candidate alignment space. As a result, AMAS runs several times faster than other state-of-the-art read mappers while achieving similar accuracy. Conclusions: AMAS provides a valuable resource to speed up the important yet time-consuming task of identifying all mapping locations of NGS reads. AMAS is implemented in C++ based on the SeqAn library and is freely available at https://sourceforge.net/projects/ngsamas/. Keywords: next-generation sequencing, read mapping, sequence alignment, adaptive seeds, seed partition, filtration
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
