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Integrated genome sizing (IGS) approach for the parallelization of whole genome analysis
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sona, Peter | - |
dc.contributor.author | Hong, Jong Hui | - |
dc.contributor.author | Lee, Sunho | - |
dc.contributor.author | Kim, Byong Joon | - |
dc.contributor.author | Hong, Woon-Young | - |
dc.contributor.author | Jung, Jongcheol | - |
dc.contributor.author | Kim, Han-Na | - |
dc.contributor.author | Kim, Hyung-Lae | - |
dc.contributor.author | Christopher, David | - |
dc.contributor.author | Herviou, Laurent | - |
dc.contributor.author | Im, Young Hwan | - |
dc.contributor.author | Lee, Kwee-Yum | - |
dc.contributor.author | Kim, Tae Soon | - |
dc.contributor.author | Jung, Jongsun | - |
dc.date.accessioned | 2019-03-12T00:41:22Z | - |
dc.date.available | 2019-03-12T09:57:49Z | - |
dc.date.issued | 2018-12-03 | - |
dc.identifier.citation | BMC Bioinformatics, 19(1):462 | ko_KR |
dc.identifier.issn | 1471-2105 | - |
dc.identifier.uri | https://hdl.handle.net/10371/146963 | - |
dc.description.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. | ko_KR |
dc.description.sponsorship | This work was supported by the INNOPOLIS Foundation, a grant-in-aid from the Korean government through Syntekabio, Inc. [grant number A2014DD101]; the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI); and the Ministry of Health & Welfare, Republic of Korea [grant number HI14C0072] The funding bodies had no role in the design, collection, analysis, or interpretation of data in this study. | ko_KR |
dc.language.iso | en | ko_KR |
dc.publisher | BioMed Central | ko_KR |
dc.subject | Genome sizing | ko_KR |
dc.subject | Sequencing | ko_KR |
dc.subject | Genome analysis | ko_KR |
dc.subject | Statistics | ko_KR |
dc.subject | Infrastructure | ko_KR |
dc.subject | Storage | ko_KR |
dc.subject | Whole genome | ko_KR |
dc.title | Integrated genome sizing (IGS) approach for the parallelization of whole genome analysis | ko_KR |
dc.type | Article | ko_KR |
dc.contributor.AlternativeAuthor | 홍정휘 | - |
dc.contributor.AlternativeAuthor | 이순호 | - |
dc.contributor.AlternativeAuthor | 김병준 | - |
dc.contributor.AlternativeAuthor | 홍운영 | - |
dc.contributor.AlternativeAuthor | 정종철 | - |
dc.contributor.AlternativeAuthor | 김한나 | - |
dc.contributor.AlternativeAuthor | 김형래 | - |
dc.contributor.AlternativeAuthor | 임영환 | - |
dc.contributor.AlternativeAuthor | 이귀염 | - |
dc.contributor.AlternativeAuthor | 김태순 | - |
dc.contributor.AlternativeAuthor | 정종선 | - |
dc.identifier.doi | 10.1186/s12859-018-2499-1 | - |
dc.language.rfc3066 | en | - |
dc.rights.holder | The Author(s). | - |
dc.date.updated | 2018-12-09T04:25:46Z | - |
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