Abstract
Genomic sequence alignment is an important technique to decode genome sequences in bioinformatics. Next-Generation Sequencing technologies produce genomic data of longer reads. Cloud platforms are adopted to address the problems arising from storage and analysis of large genomic data. Existing genes sequencing tools for cloud platforms predominantly consider short read gene sequences and adopt the Hadoop MapReduce framework for computation. However, serial execution of map and reduce phases is a problem in such systems. Therefore, in this paper, we introduce Burrows-Wheeler Aligner's Smith-Waterman Alignment on Parallel MapReduce (BWASW-PMR) cloud platform for long sequence alignment. The proposed cloud platform adopts a widely accepted and accurate BWA-SW algorithm for long sequence alignment. A custom MapReduce platform is developed to overcome the drawbacks of the Hadoop framework. A parallel execution strategy of the MapReduce phases and optimization of Smith-Waterman algorithm are considered. Performance evaluation results exhibit an average speed-up of 6.7 considering BWASW-PMR compared with the state-of-the-art Bwasw-Cloud. An average reduction of 30% in the map phase makespan is reported across all experiments comparing BWASW-PMR with Bwasw-Cloud. Optimization of Smith-Waterman results in reducing the execution time by 91.8%. The experimental study proves the efficiency of BWASW-PMR for aligning long genomic sequences on cloud platforms.
Highlights
Bioinformatics involves the biological, genomic, statistics, mathematics, and computer science disciplines of study
The parallel executions of map and reduce phases along with SW optimization are the main contributing factors to the speed-up observed in this study
The existing long read aligners that adopt the cloud platform for computation suffer from drawbacks that are discussed in this paper
Summary
Bioinformatics involves the biological, genomic, statistics, mathematics, and computer science disciplines of study. Identification of similarities/dissimilarities is achieved by sequence comparison algorithms. Comparisons of biological sequences produce matching alignments and similarity scores. These similarity scores represent the similarities/dissimilarities between the considered biological sequences. The matching alignments and similarity scores are used for secondary structure predictions and multiple sequence alignments which are highly complex operations that rely on the accuracy of the comparison algorithm used. Applications related to cancer research, forensics, agrigenomics, genetic disease identification, microbial research, reproductive health, human wholegenome sequencing, and many more rely on sequence alignment algorithms for analysis. Burrows-Wheeler Aligner’s Smith-Waterman (BWA-SW) Alignment relies on SW algorithm to align the seed matches of sequences. Let q0 and q1 represent two genomic sequences obtained from the seed matches. The Smith-Waterman algorithm computes the similarity matrix score initially. The remaining elements of Z (indexed by (a, b)) are computed using
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