Difference between revisions of "GimmeMotifs"
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|sw 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. | |sw 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. | ||
|bio domain=Transcription regulation | |bio domain=Transcription regulation | ||
| − | |bio method=ChIP-seq, | + | |bio method=ChIP-seq, Motif finding, Motif analysis |
|bio tech=Any | |bio tech=Any | ||
|created by=Simon van Heeringen | |created by=Simon van Heeringen | ||
Revision as of 09:05, 9 July 2010
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Application data |
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| Created by | Simon van Heeringen |
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| Biological application domain(s) | Transcription regulation |
| Principal bioinformatics method(s) | ChIP-seq, Motif finding, Motif analysis |
| Technology | Any |
| Created at | NCMLS, Nijmegen, the Netherlands |
| Maintained? | Yes |
| Input format(s) | BED |
| Output format(s) | PSSM, HTML |
| Programming language(s) | Python |
| Licence | MIT |
| 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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