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

• Published in: Bioinformatics Vol. 27; no. 2; pp. 270 - 271
• Main Authors: van Heeringen, Simon J., Veenstra, Gert Jan C.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 15.01.2011
• Subjects: Algorithms; Animals; Applications Note; Binding; Binding Sites; Bioinformatics; Biological and medical sciences; Chromatin Immunoprecipitation; Computation; Computational Biology - methods; Computer programs; Enrichment; Fundamental and applied biological sciences. Psychology; General aspects; Humans; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Mice; Pipelines; Sequence Analysis, DNA; Similarity; Software; Source code; Transcription Factors - metabolism; Sequencing; De novo; Prediction; Pipeline
• ISSN: 1367-4803; 1367-4811; 1367-4811; 1460-2059
• DOI: 10.1093/bioinformatics/btq636

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.