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

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons50595

Thomas-Chollier,  Morgane
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Thomas-Chollier.pdf
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Zitation

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.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000E-E86A-4
Zusammenfassung
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.