HASV : hadoop-based NGS analyzer for predicting genomic structure variations

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dc.contributor.authorKunhwan Go-
dc.contributor.authorJongcheol Yoon-
dc.contributor.authorK Park-
dc.date.accessioned2017-04-19T09:57:51Z-
dc.date.available2017-04-19T09:57:51Z-
dc.date.issued2014-
dc.identifier.issn1876-1100-
dc.identifier.uri10.1007/978-3-642-40675-1-49ko
dc.identifier.urihttps://oak.kribb.re.kr/handle/201005/12250-
dc.description.abstractThe NGS technology produces large scale biologic data sets much cheaper and faster than the previous methods. As it is almost impossible to store or analyze such large scale NGS data with a traditional method on a commodity server, many problems arise. Hadoop is an alternative to this requirement. We aim to address the issues involved in the large scale data analysis on the cloud in bioinformatics. Accordingly, we propose analysis service for predicting genome structural variations associated with diseases by using Hadoop. The result of this study reveals that the system proposed in this study efficiently predicts genomic variations from large scale data sets. ⓒ Springer-Verlag Berlin Heidelberg 2014.-
dc.publisherSpringer Verlag (Germany)ko
dc.titleHASV : hadoop-based NGS analyzer for predicting genomic structure variations-
dc.title.alternativeHASV : hadoop-based NGS analyzer for predicting genomic structure variations-
dc.typeArticle-
dc.citation.titleLecture Notes in Electrical Engineering-
dc.citation.number0-
dc.citation.endPage327-
dc.citation.startPage321-
dc.citation.volume274-
dc.contributor.affiliatedAuthorKunhwan Go-
dc.contributor.affiliatedAuthorJongcheol Yoon-
dc.contributor.alternativeName고건환-
dc.contributor.alternativeName윤종철-
dc.contributor.alternativeName박경석-
dc.identifier.bibliographicCitationLecture Notes in Electrical Engineering, vol. 274, pp. 321-327-
dc.identifier.doi10.1007/978-3-642-40675-1-49-
dc.description.journalClassN-
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