Difference between revisions of "GimmeMotifs"
m (Text replace - "ChIP-Seq" to "ChIP-seq") |
m (Text replace - "Sequence motif analysis" to "Sequence motif comparison") |
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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=Gene regulation, ChIP-seq, Epigenomics | |bio domain=Gene regulation, ChIP-seq, Epigenomics | ||
| − | |bio method=Sequence motif | + | |bio method=Sequence motif comparison, |
|bio tech=Any | |bio tech=Any | ||
|created by=Simon van Heeringen | |created by=Simon van Heeringen | ||
Latest revision as of 20:39, 2 February 2016
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Application data |
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| Created by | Simon van Heeringen |
|---|---|
| Biological application domain(s) | Gene regulation, ChIP-seq, Epigenomics |
| Principal bioinformatics method(s) | Sequence motif comparison |
| 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 |
| 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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