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dc.date.accessioned2020-06-05T19:12:47Z
dc.date.available2020-06-05T19:12:47Z
dc.date.created2019-06-06T13:01:52Z
dc.date.issued2019
dc.identifier.citationGrytten, Ivar Rand, Knut Dagestad Nederbragt, Alexander Johan Storvik, Geir Olve Glad, Ingrid Kristine Sandve, Geir Kjetil . Graph Peak Caller: Calling ChIP-seq peaks on graph-based reference genomes. PLoS Computational Biology. 2019, 15(2), 1-13
dc.identifier.urihttp://hdl.handle.net/10852/76717
dc.description.abstractGraph-based representations are considered to be the future for reference genomes, as they allow integrated representation of the steadily increasing data on individual variation. Currently available tools allow de novo assembly of graph-based reference genomes, alignment of new read sets to the graph representation as well as certain analyses like variant calling and haplotyping. We here present a first method for calling ChIP-Seq peaks on read data aligned to a graph-based reference genome. The method is a graph generalization of the peak caller MACS2, and is implemented in an open source tool, Graph Peak Caller. By using the existing tool vg to build a pan-genome of Arabidopsis thaliana, we validate our approach by showing that Graph Peak Caller with a pan-genome reference graph can trace variants within peaks that are not part of the linear reference genome, and find peaks that in general are more motif-enriched than those found by MACS2.
dc.languageEN
dc.publisherPublic Library of Science (PLoS)
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleGraph Peak Caller: Calling ChIP-seq peaks on graph-based reference genomes
dc.typeJournal article
dc.creator.authorGrytten, Ivar
dc.creator.authorRand, Knut Dagestad
dc.creator.authorNederbragt, Alexander Johan
dc.creator.authorStorvik, Geir Olve
dc.creator.authorGlad, Ingrid Kristine
dc.creator.authorSandve, Geir Kjetil
cristin.unitcode185,15,5,35
cristin.unitnameForskningsgruppen for biomedisinsk informatikk
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2
dc.identifier.cristin1703165
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=PLoS Computational Biology&rft.volume=15&rft.spage=1&rft.date=2019
dc.identifier.jtitlePLoS Computational Biology
dc.identifier.volume15
dc.identifier.issue2
dc.identifier.doihttps://doi.org/10.1371/journal.pcbi.1006731
dc.identifier.urnURN:NBN:no-79836
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.source.issn1553-734X
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/76717/2/journal.pcbi.1006731.pdf
dc.type.versionPublishedVersion
cristin.articleide1006731


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