Integrated genome sizing (IGS) approach for the parallelization of whole genome analysis, BMC Bioinformatics (2019)

Integrated genome sizing (IGS) approach for the parallelization of whole genome analysis.

Peter Sona[1], Jong Hui Hong[2], Sunho Lee[3], Byong Joon Kim[4], Woon-Young Hong[5], Jongcheol Jung[6], Han-Na Kim[7], Hyung-Lae Kim[8], David Christopher[9], Laurent Herviou[10], Young Hwan Im[11], Kwee-Yum Lee[12], Tae Soon Kim[13] and Jongsun Jung[14]

[1] [2] [3] [4] [5] [6] [14] Genome Data Integration Centre, Syntekabio Inc., 187 Techno 2-ro, B512, Yuseong-gu, Daejeon, 34025, South Korea.

[12] [13] Gwanghwamun Medical Study Centre, Syntekabio Inc., 92 Saemunan-ro, #1708, Jongno-gu, Seoul, 03186, South Korea.

[7] [8] Department of Biochemistry, School of Medicine, Ewha Woman's University, Seoul 07985, Republic of Korea

Abstract

The use of whole genome sequence has increased recently with rapid progression of next-generation sequencing (NGS) technologies. However, storing raw sequence reads to perform large-scale genome analysis pose hardware challenges. Despite advancement in genome analytic platforms, efficient approaches remain relevant especially as applied to the human genome. In this study, an Integrated Genome Sizing (IGS) approach is adopted to speed up multiple whole genome analysis in high-performance computing (HPC) environment. The approach splits a genome (GRCh37) into 630 chunks (fragments) wherein multiple chunks can simultaneously be parallelized for sequence analyses across cohorts.

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http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2499-1