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의과대학
의학과
Journal papers
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Integrated genome sizing (IGS) approach for the parallelization of whole genome analysis
Title
Integrated genome sizing (IGS) approach for the parallelization of whole genome analysis
Authors
Sona P.
;
Hong J.H.
;
Lee S.
;
Kim B.J.
;
Hong W.-Y.
;
Jung J.
;
Kim H.-N.
;
Kim H.-L.
;
Christopher D.
;
Herviou L.
;
Im Y.H.
;
Lee K.-Y.
;
Kim T.S.
Ewha Authors
김형래
;
김한나
SCOPUS Author ID
김형래
; 김한나
Issue Date
2018
Journal Title
BMC Bioinformatics
ISSN
1471-2105
Citation
BMC Bioinformatics vol. 19, no. 1
Keywords
Genome analysis
;
Genome sizing
;
Infrastructure
;
Sequencing
;
Statistics
;
Storage
;
Whole genome
Publisher
BioMed Central Ltd.
Indexed
SCIE; SCOPUS
Document Type
Article
Abstract
Background: 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. Results: IGS was integrated on Maha-Fs (HPC) system, to provide the parallelization required to analyze 2504 whole genomes. Using a single reference pilot genome, NA12878, we compared the NGS process time between Maha-Fs (NFS SATA hard disk drive) and SGI-UV300 (solid state drive memory). It was observed that SGI-UV300 was faster, having 32.5 mins of process time, while that of the Maha-Fs was 55.2 mins. Conclusions: The implementation of IGS can leverage the ability of HPC systems to analyze multiple genomes simultaneously. We believe this approach will accelerate research advancement in personalized genomic medicine. Our method is comparable to the fastest methods for sequence alignment. © 2018 The Author(s).
DOI
10.1186/s12859-018-2499-1
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