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  RSAT peak-motifs: motif analysis in full-size ChIP-seq datasets

Thomas-Chollier, M., Herrmann, C., Defrance, M., Sand, O., Thieffry, D., & van Helden, J. (2012). RSAT peak-motifs: motif analysis in full-size ChIP-seq datasets. Nucleic Acids Research (London), 40(4), e31-e31. doi:10.1093/nar/gkr1104.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-000E-E86A-4 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-000E-E86B-2
Genre: Journal Article

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 Creators:
Thomas-Chollier, Morgane1, Author              
Herrmann, Carl2, Author
Defrance, Matthieu3, Author
Sand, Olivier4, Author
Thieffry, Denis2, 5, Author
van Helden, Jacques2, 6, Author
Affiliations:
1Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, Berlin, Germany, ou_1433547              
2Technological Advances for Genomics and Clinics (TAGC), INSERM U928 & Université de la Méditerranée, Campus de Luminy, Marseille F-13288, France, ou_persistent22              
3Centro de Ciencias Genomicas, Universidad Nacional Autónoma de México, Avenida Universidad, Cuernavaca, Morelos 62210, Mexico, ou_persistent22              
4CNRS-UMR8199 Institut de Biologie de Lille, Génomique et maladies métaboliques, 1, rue du Pr Calmette, Lille 59000, ou_persistent22              
5Institut de Biologie de l'Ecole Normale Supérieure – UMR ENS & CNRS 8197 & INSERM 1024, 46 rue d'Ulm, Paris 75005, France, ou_persistent22              
6Laboratoire de Bioinformatique des Génomes et des Réseaux (BiGRe), Université Libre de Bruxelles, Campus Plaine, CP 263, Bld du Triomphe, Bruxelles B-1050, Belgium , ou_persistent22              

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Free keywords: Animals *Chromatin Immunoprecipitation Embryonic Stem Cells/metabolism Mice Nucleotide Motifs *Regulatory Elements, Transcriptional *Sequence Analysis, DNA *Software Transcription Factors/metabolism User-Computer Interface p300-CBP Transcription Factors/metabolism
 Abstract: ChIP-seq is increasingly used to characterize transcription factor binding and chromatin marks at a genomic scale. Various tools are now available to extract binding motifs from peak data sets. However, most approaches are only available as command-line programs, or via a website but with size restrictions. We present peak-motifs, a computational pipeline that discovers motifs in peak sequences, compares them with databases, exports putative binding sites for visualization in the UCSC genome browser and generates an extensive report suited for both naive and expert users. It relies on time- and memory-efficient algorithms enabling the treatment of several thousand peaks within minutes. Regarding time efficiency, peak-motifs outperforms all comparable tools by several orders of magnitude. We demonstrate its accuracy by analyzing data sets ranging from 4000 to 1,28,000 peaks for 12 embryonic stem cell-specific transcription factors. In all cases, the program finds the expected motifs and returns additional motifs potentially bound by cofactors. We further apply peak-motifs to discover tissue-specific motifs in peak collections for the p300 transcriptional co-activator. To our knowledge, peak-motifs is the only tool that performs a complete motif analysis and offers a user-friendly web interface without any restriction on sequence size or number of peaks.

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Language(s): eng - English
 Dates: 2011-12-082012
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1093/nar/gkr1104
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Title: Nucleic Acids Research (London)
  Other : Nucleic Acids Res
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 40 (4) Sequence Number: - Start / End Page: e31 - e31 Identifier: ISSN: 0305-1048
CoNE: /journals/resource/110992357379342