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Genome sequence analysis with monetdb: a case study on ebola virus diversity

dc.contributor.authorCijvat, Robin
dc.contributor.authorManegold, Stefan
dc.contributor.authorKersten, Martin
dc.contributor.authorKlau, Gunnar W.
dc.contributor.authorSchönhuth, Alexander
dc.contributor.authorMarschall, Tobias
dc.contributor.authorZhang, Ying
dc.contributor.editorRitter, Norbert
dc.contributor.editorHenrich, Andreas
dc.contributor.editorLehner, Wolfgang
dc.contributor.editorThor, Andreas
dc.contributor.editorFriedrich, Steffen
dc.contributor.editorWingerath, Wolfram
dc.date.accessioned2017-06-30T11:39:35Z
dc.date.available2017-06-30T11:39:35Z
dc.date.issued2015
dc.description.abstractNext-generation sequencing (NGS) technology has led the life sciences into the big data era. Today, sequencing genomes takes little time and cost, but results in terabytes of data to be stored and analysed. Biologists are often exposed to excessively time consuming and error-prone data management and analysis hurdles. In this paper, we propose a database management system (DBMS) based approach to accelerate and substantially simplify genome sequence analysis. We have extended MonetDB, an open-source column-based DBMS, with a BAM module, which enables easy, flexible, and rapid management and analysis of sequence alignment data stored as Sequence Alignment/Map (SAM/BAM) files. We describe the main features of MonetDB/BAM using a case study on Ebola virus genomes.en
dc.identifier.isbn978-3-88579-636-7
dc.identifier.pissn1617-5468
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofDatenbanksysteme für Business, Technologie und Web (BTW 2015) - Workshopband
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-242
dc.titleGenome sequence analysis with monetdb: a case study on ebola virus diversityen
dc.typeText/Conference Paper
gi.citation.endPage150
gi.citation.publisherPlaceBonn
gi.citation.startPage143
gi.conference.date2.-3. März 2015
gi.conference.locationHamburg

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