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XB-ART-46556
Bioinformatics. January 15, 2011; 27 (2): 270-1.

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

van Heeringen SJ , Veenstra GJ .


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.

PubMed ID: 21081511
PMC ID: PMC3018809
Article link: Bioinformatics.
Grant support: R01 HD054356-04 NICHD NIH HHS , R01HD054356 NICHD NIH HHS

Genes referenced: tbx2 tp63


References:
Carlson, 2007, Pubmed[+]


Article Images: [+] show captions

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