Difference between revisions of "ChIPmeta"
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{{Bioinformatics application | {{Bioinformatics application | ||
|sw summary=Combining data from ChIP-seq and ChIP-chip. | |sw summary=Combining data from ChIP-seq and ChIP-chip. | ||
| − | |bio domain=Transcription factors and regulatory sites, ChIP- | + | |bio domain=Transcription factors and regulatory sites, ChIP-seq, ChIP-on-chip, |
| − | |bio method=Peak calling, | + | |bio method=Peak calling, |
| + | |interface= Command line | ||
| + | |resource type=Command-line tool, | ||
}} | }} | ||
In HHMM, inference results from individual HMMs in ChIP-seq and ChIP-chip experiments are summarized by a higher level HMM. Simulation studies show the advantage of HHMM when data from both technologies co-exist. Analysis of two well-studied transcription factors, NRSF and CTCF, also suggests that HHMM yields improved TFBS identification in comparison to analyses using individual data sources or a simple merger of the two. | In HHMM, inference results from individual HMMs in ChIP-seq and ChIP-chip experiments are summarized by a higher level HMM. Simulation studies show the advantage of HHMM when data from both technologies co-exist. Analysis of two well-studied transcription factors, NRSF and CTCF, also suggests that HHMM yields improved TFBS identification in comparison to analyses using individual data sources or a simple merger of the two. | ||
| + | {{Links}} | ||
| + | {{References}} | ||
| + | {{Link box}} | ||
Latest revision as of 12:11, 3 November 2016
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Application data |
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| Biological application domain(s) | Transcription factors and regulatory sites, ChIP-seq, ChIP-on-chip |
|---|---|
| Principal bioinformatics method(s) | Peak calling |
| Maintained? | Maybe |
| Interface type(s) | Command line |
| Resource type(s) | Command-line tool |
Summary: Combining data from ChIP-seq and ChIP-chip.
"Error: no local variable "counter" was set." is not a number.
In HHMM, inference results from individual HMMs in ChIP-seq and ChIP-chip experiments are summarized by a higher level HMM. Simulation studies show the advantage of HHMM when data from both technologies co-exist. Analysis of two well-studied transcription factors, NRSF and CTCF, also suggests that HHMM yields improved TFBS identification in comparison to analyses using individual data sources or a simple merger of the two.
Links
References
To add a reference for ChIPmeta, enter the PubMed ID in the field below and click 'Add'.
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