Parallelized genomic sequencing model: A big data approach for bioinformatics application

Genomic sequence alignment is one of the most significant applications in bioinformatics. In future gene sequencing technologies are expected to produce terabyte of genomic data. Cloud Computing and MapReduce framework play an important role in bioinformatics intensive application in achieving parallelization since it provides a consistent performance over time and it provides good fault tolerant mechanism. The existing sequencing methodologies are based on Hadoop MapReduce Framework which adopts a serial execution strategy which is an area of concern. This work introduces a Smith-Waterman Alignment on the Parallel Azure Map Reduce (SW-PAMR) Cloud platform for bioinformatics sequence alignment. This work adopts a widely accepted and accurate Smith-Waterman algorithm for sequence alignment and parallelization methodology of Map and Reduce framework. A customised MapReduce based on Azure Cloud platform is developed to overcome the issue in Hadoop MapReduce framework. The experimental study presented in this work proves that the SW-PAMR can accurately and effectively align bioinformatics genomic sequences.