Difference between revisions of "GimmeMotifs"
Line 3: | Line 3: | ||
|bio domain=Transcription regulation, ChIP-Seq, Epigenomics | |bio domain=Transcription regulation, ChIP-Seq, Epigenomics | ||
|bio method=Motif analysis | |bio method=Motif analysis | ||
− | |bio tech= | + | |bio tech=any |
|created by=Simon van Heeringen | |created by=Simon van Heeringen | ||
|created at=NCMLS, Nijmegen, the Netherlands | |created at=NCMLS, Nijmegen, the Netherlands |
Revision as of 09:22, 15 September 2011
Application data |
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Created by | Simon van Heeringen |
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Biological application domain(s) | Transcription regulation, ChIP-Seq, Epigenomics |
Principal bioinformatics method(s) | Motif analysis |
Technology | any |
Created at | NCMLS, Nijmegen, the Netherlands |
Maintained? | Yes |
Input format(s) | BED, FASTA |
Output format(s) | PSSM, HTML |
Programming language(s) | Python |
Licence | MIT License |
Operating system(s) | Linux |
Summary: GimmeMotifs is a de novo motif prediction pipeline, especially suited for ChIP-seq datasets. It incorporates several existing motif prediction algorithms in an ensemble method to predict motifs and clusters these motifs using the WIC similarity scoring metric.
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Description
GimmeMotifs runs several different algorithms (as was suggested in some benchmark studies and reviews) and combines the output into a non-redundant list of motifs. Long-time favorites are included, as well as some more recent tools developed for ChIP-seq (or ChIP-chip) data. Current supported tools are: MDmodule, MEME, Weeder, GADEM, MotifSampler, trawler, Improbizer, MoAn and BioProspector. To rank and evaluate the motifs we predict motifs on a part of the dataset, and use the rest for evaluation (enrichment, ROC curve, MNCP score). An extensive HTML report is generated to visualize the resuls.
Links
References
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