A MapReduce Framework for DNA Sequencing Data Processing

Samy Ghoneimy, Samir Abou El-Seoud

Abstract


Genomics and Next Generation Sequencers (NGS) like Illumina Hiseq produce data in the order of 200 billion base pairs in a single one-week run for a 60x human genome coverage, which requires modern high-throughput experimental technologies that can only be tackled with high performance computing (HPC) and specialized software algorithms called “short read aligners”. This paper focuses on the implementation of the DNA sequencing as a set of MapReduce programs that will accept a DNA data set as a FASTQ file and finally generate a VCF (variant call format) file, which has variants for a given DNA data set. In this paper MapReduce/Hadoop along with Burrows-Wheeler Aligner (BWA), Sequence Alignment/Map (SAM) tools, are fully utilized to provide various utilities for manipulating alignments, including sorting, merging, indexing, and generating alignments. The Map-Sort-Reduce process is designed to be suited for a Hadoop framework in which each cluster is a traditional N-node Hadoop cluster to utilize all of the Hadoop features like HDFS, program management and fault tolerance. The Map step performs multiple instances of the short read alignment algorithm (BoWTie) that run in parallel in Hadoop. The ordered list of the sequence reads are used as input tuples and the output tuples are the alignments of the short reads. In the Reduce step many parallel instances of the Short Oligonucleotide Analysis Package for SNP (SOAPsnp) algorithm run in the cluster. Input tuples are sorted alignments for a partition and the output tuples are SNP calls. Results are stored via HDFS, and then archived in SOAPsnp format. The proposed framework enables extremely fast discovering somatic mutations, inferring population genetical parameters, and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. It also demonstrate that this method achieves comparable accuracy to alternative methods for sequencing data processing.



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International Journal of Recent Contributions from Engineering, Science & IT (iJES) – eISSN: 2197-8581
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