XB-ART-46556Bioinformatics January 15, 2011; 27 (2): 270-1.
GimmeMotifs: a de novo motif prediction pipeline for ChIP-sequencing experiments.
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://email@example.comSupplementary data are available at Bioinformatics online.
PubMed ID: 21081511
PMC ID: PMC3018809
Article link: Bioinformatics
Species referenced: Xenopus
Genes referenced: tbx2 tp63
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References [+] :
Carlson, SCOPE: a web server for practical de novo motif discovery. 2007, Pubmed