RSAT peak-motifs
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Application data |
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| Created by | Jacques van Helden, Morgane Thomas-Chollier, Matthieu Defrance, Carl Herrmann, Denis Thieffry, Olivier Sand |
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| Biological application domain(s) | ChIP-seq, Gene regulation, Epigenomics |
| Principal bioinformatics method(s) | Sequence motif discovery, Sequence motif recognition, Sequence motif comparison |
| Technology | Any |
| Created at | Université Libre de Bruxelles, Université de la Méditerrannée, Max Planck Institute for Molecular Genetics, Pasteur, IBENS |
| Maintained? | Yes |
| Input format(s) | Fasta |
| Output format(s) | HTML, text, graphics (png) |
| Programming language(s) | Perl, CGI, Python, C |
| Software libraries | None required for the Web interface. Auto installation script for the stand-alone version of RSAT. |
| Licence | Commercial, Freeware |
| Operating system(s) | UNIX, Mac OS X, Linux |
Summary: A workflow combining a series of time- and memory-efficient motif analysis tools to extract motifs from full-size collections of peaks as generated by ChIP-seq, ChIP-chip or other ChIP-X technologies.
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Description
The peak-motif workflow is integrated in the software suite "Regulatory Sequence Analysis Tools" (RSAT).
It combines:
- analysis of sequence composition (peak lengths, global and positional distribution of nucleotide and dinucleotide frequencies);
- motif discovery, based on complementary criteria: global over-representation of words (oligo-analysis) and spaced pairs (dyad-analysis), heterogeneity in word positional distributions (position-analysis), detection of local enrichment of words in positional windows of centered peaks (local-words);
- motif comparison: discovered motifs are compared with databases of known motifs (JASPAR, RegulonDB) or with user-supplied motifs;
- binding site prediction: matching of the discovered motifs against peak sequences;
- visualization: predicted sites in bed format, suitable for the UCSC genome browser;
The tool presents two modes of utilization:
- Single set analysis (peak sequences): when a single set of peak sequences is provided, the program discovers motifs that are “intrinsically" over-represented, i.e. more frequent than what can be expected from the sequence composition in olionucleotides. Background models of various stringency can be chosen (higher order Markov models ensure more specificity but reduce sensitivity for small data sets).
- Differential analysis (test versus control): when two sets of peak sequences are provided, the program discovers motifs that are significantly more frequent in the test than in the control set.
The workflow is implemented in Perl, the Web interface in CGI, motif analysis algorithms in combinations of Perl, python and C.
Links
References
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