biostudies-literaturevan Heeringen SJNICHD NIH HHS270-1https://www.ebi.ac.uk/biostudies/studies/S-EPMC3018809biostudies-literatureUnknown27(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 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.<h4>AvailabilitBioinformatics (Oxford, England)GimmeMotifs: a de novo motif prediction pipeline for ChIP-sequencing experiments.PMC3018809R01HD054356R01 HD054356R01 HD054356-04Veenstra GJvan Heeringen SJfalseGimmeMotifs: a de novo motif prediction pipeline for ChIP-sequencing experiments.<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 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.<h4>Availabilit2011-01-01T00:00:00Z2011 Jan2025-04-26T05:09:34.033Z2019-03-27T00:38:00ZS-EPMC30188092108151110.1093/bioinformatics/btq636