AMAS: Optimizing the Partition and Filtration of Adaptive Seeds to Speed up Read Mapping

Abstract

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