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GimmeMotifs: a de novo motif prediction pipeline for ChIP-sequencing experiments.


ABSTRACT: Accurate prediction of transcription factor binding motifs that are enriched in a collection of sequences remains a computational challenge. Here we report on GimmeMotifs, a pipeline that incorporates an ensemble of computational tools to predict motifs de novo from ChIP-sequencing (ChIP-seq) data. Similar redundant motifs are compared using the weighted information content (WIC) similarity score and clustered using an iterative procedure. A comprehensive output report is generated with several different evaluation metrics to compare and evaluate the results. Benchmarks show that the method performs well on human and mouse ChIP-seq datasets. GimmeMotifs consists of a suite of command-line scripts that can be easily implemented in a ChIP-seq analysis pipeline.GimmeMotifs is implemented in Python and runs on Linux. The source code is freely available for download at http://www.ncmls.eu/bioinfo/gimmemotifs/.s.vanheeringen@ncmls.ru.nlSupplementary data are available at Bioinformatics online.

SUBMITTER: van Heeringen SJ 

PROVIDER: S-EPMC3018809 | biostudies-other | 2011 Jan

REPOSITORIES: biostudies-other

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GimmeMotifs: a de novo motif prediction pipeline for ChIP-sequencing experiments.

van Heeringen Simon J SJ   Veenstra Gert Jan C GJ  

Bioinformatics (Oxford, England) 20101115 2


<h4>Summary</h4>Accurate prediction of transcription factor binding motifs that are enriched in a collection of sequences remains a computational challenge. Here we report on GimmeMotifs, a pipeline that incorporates an ensemble of computational tools to predict motifs de novo from ChIP-sequencing (ChIP-seq) data. Similar redundant motifs are compared using the weighted information content (WIC) similarity score and clustered using an iterative procedure. A comprehensive output report is generat  ...[more]

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