Processing next generation sequencing data in map-reduce framework using hadoop-BAM in a computer cluster

Next-Generation Sequencing in bioinformatics produce a massive amount of data volume. Big data technologies are needed to reduce computation time in data processing. In this paper, we implement Hadoop Map-Reduce framework for processing Next-Generation Sequencing using Hadoop-BAM library. Our implementation process a Binary Alignment Map (BAM) file which contains a reference sequence and many aligned/not-aligned reads by spitting the BAM file into Hadoop data blocks. To process the BAM file in a computer cluster, we implement a mapper and a reducer of Hadoop Map-Reduce framework. The mapper processes the BAM file to produce key value pairs. While, the reducer summary the key value pairs into a meaningful output. Here the mapper and reducer are created to summarize the number of bases in a BAM file. We conduct the experiment in a LIPI Hadoop cluster. The cluster consists of 96 CPU cores. The result of our experiments show that our map-reduce implementations are gaining speed-up compare to serial Next-Generation Sequencing with Picard tools.