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Bioinformatics. 2011 Jan 15;27(2):270-1. doi: 10.1093/bioinformatics/btq636. Epub 2010 Nov 15.

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

Author information

1
Department of Molecular Biology, Faculty of Science, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands. s.vanheeringen@ncmls.ru.nl

Abstract

SUMMARY:

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.

AVAILABILITY:

GimmeMotifs is implemented in Python and runs on Linux. The source code is freely available for download at http://www.ncmls.eu/bioinfo/gimmemotifs/.

CONTACT:

s.vanheeringen@ncmls.ru.nl

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
21081511
PMCID:
PMC3018809
DOI:
10.1093/bioinformatics/btq636
[Indexed for MEDLINE]
Free PMC Article

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