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