Difference between revisions of "ChIPmeta"
m (Text replace - "ChIP-Chip" to "ChIP-on-chip") |
m (Text replace - "([= ])Transcription Factor Binding Site identification," to "$1Transcription factors and regulatory sites,") |
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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 | + | |bio domain=Transcription factors and regulatory sites, ChIP-Seq, ChIP-on-chip, |
|bio method=Hidden Markov Model | |bio method=Hidden Markov Model | ||
}} | }} | ||
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. | ||
Revision as of 19:15, 5 November 2015
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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) | Hidden Markov Model |
| Maintained? | Maybe |
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.
