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dc.contributor.authorGrau, Jan
dc.contributor.authorPosch, Stefan
dc.contributor.authorGrosse, Ivo
dc.contributor.authorKeilwagen, Jens
dc.contributor.editorGiegerich, Robert
dc.contributor.editorHofestädt, Ralf
dc.contributor.editorNattkemper, Tim W.
dc.date.accessioned2017-07-26T14:12:12Z
dc.date.available2017-07-26T14:12:12Z
dc.date.issued2014
dc.identifier.isbn978-3-88579-629-9
dc.identifier.issn1617-5468
dc.description.abstractHigh-throughput techniques like ChIP-seq, ChIP-exo, and protein binding microarrays (PBMs) demand for novel de novo motif discovery approaches that focus on accuracy and runtime on large data sets. While specialized algorithms have been designed for discovering motifs in in-vivo ChIP-seq/ChIP-exo or in in-vitro PBM data, none of these works equally well for all these high-throughput techniques. Here, we present Dimont, a general approach for fast and accurate de-novo motif discovery from high-throughput data, which achieves a competitive performance on both ChIP-seq and PBM data compared to recent approaches specifically designed for either technique. Hence, Dimont allows for investigating differences between in-vitro and in-vivo binding in an unbiased manner using a unified approach. For most transcription factors, Dimont discovers similar motifs from in-vivo and in-vitro data, but we also find notable exceptions. Scrutinizing the benefit of modeling dependencies between binding site positions, we find that more complex motif models often increase prediction performance and, hence, are a worthwhile field of research. Original paper: doi: 10.1093/nar/gkt831en
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofGerman conference on bioinformatics 2014
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-235
dc.titleA general approach for discriminative de novo motif discovery from high-throughput dataen
dc.typeText/Conference Paper
dc.pubPlaceBonn
mci.reference.pages41-43
mci.conference.locationBielefeld
mci.conference.date28. September - 1. October 2014


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