IMOS: Improved Meta-aligner and Minimap2 on Spark

Abstract

Background: Long reads provide valuable information regarding the sequence composition of genomes. Long reads are usually very noisy which renders their alignments on the reference genome a daunting task. It may take days to process datasets enough to sequence a human genome on a single node. Hence, it is of primary importance to have an aligner which can operate on distributed clusters of computers with high performance in accuracy and speed. Results: In this paper, we presented IMOS, an aligner for mapping noisy long reads to the reference genome. It can be used on a single node as well as on distributed nodes. In its single-node mode, IMOS is an Improved version of Meta-aligner (IM) enhancing both its accuracy and speed. IM is up to 6x faster than the original Meta-aligner. It is also implemented to run IM and Minimap2 on Apache Spark for deploying on a cluster of nodes. Moreover, multi-node IMOS is faster than SparkBWA while executing both IM (1.5x) and Minimap2 (25x). Conclusion: In this paper, we purposed an architecture for mapping long reads to a reference. Due to its implementation, IMOS speed can increase almost linearly with respect to the number of nodes in a cluster. Also, it is a multi-platform application able to operate on Linux, Windows, and macOS.