{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["van Heeringen SJ"],"funding":["NICHD NIH HHS"],"pagination":["270-1"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC3018809"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["27(2)"],"pubmed_abstract":["<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>Availabilit"],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["GimmeMotifs: a de novo motif prediction pipeline for ChIP-sequencing experiments."],"pmcid":["PMC3018809"],"funding_grant_id":["R01HD054356","R01 HD054356","R01 HD054356-04"],"pubmed_authors":["Veenstra GJ","van Heeringen SJ"],"additional_accession":[]},"is_claimable":false,"name":"GimmeMotifs: a de novo motif prediction pipeline for ChIP-sequencing experiments.","description":"<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>Availabilit","dates":{"release":"2011-01-01T00:00:00Z","publication":"2011 Jan","modification":"2025-04-26T05:09:34.033Z","creation":"2019-03-27T00:38:00Z"},"accession":"S-EPMC3018809","cross_references":{"pubmed":["21081511"],"doi":["10.1093/bioinformatics/btq636"]}}