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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