Accelerating Pairwise Sequence Alignment Algorithm by MapReduce Technique for Next-Generation Sequencing (NGS) Data Analysis

Abstract

Next-generation sequencing (NGS) technologies and different types of sequencing machines are introduced in an enormous volume of omics data. For analysis of NGS data, sequence alignment is always an essential step in finding relationships between sequences. Pairwise sequence alignment is a challenging task for reasonably large input sequences. Smith–Waterman (SW) is a popular centralized algorithm for sequence alignment. However, as data is spreading expeditiously, conventional centralized sequence alignment tools are inefficient in terms of computational time. In this paper, we propose a distributed pairwise sequence alignment technique using MapReduce implemented on Apache Spark framework, called MRaligner. We have compared the result of the proposed MRaligner with Jaligner, an open-source Java implementation of the Smith–Waterman algorithm for biological sequence alignment, and found significant improvement in terms of computational time.