title: GimmeMotifs: a de novo motif prediction pipeline for ChIP-sequencing experiments.
abstract: CONCLUSIONS
  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.
  
  
  BACKGROUND
  GimmeMotifs is implemented in Python and runs on Linux. The source code is freely available for download at http://www.ncmls.eu/bioinfo/gimmemotifs/.
  
  
  BACKGROUND
  s.vanheeringen@ncmls.ru.nl
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Molecular Biology, Faculty of Science, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands. s.vanheeringen@ncmls.ru.nl
authors.forename: Simon J
authors.initials: SJ
authors.lastname: van Heeringen
authors.forename: Gert Jan C
authors.initials: GJ
authors.lastname: Veenstra
mesh_terms: D000465:Algorithms; D000818:Animals; D001665:Binding Sites; D047369:Chromatin Immunoprecipitation; D019295:Computational Biology / Q000379:methods; D006801:Humans; D051379:Mice; D017422:Sequence Analysis, DNA; D012984:Software; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D014157:Transcription Factors