Difference between revisions of "ChIPmeta"
m (Text replace - "ChIP-Seq" to "ChIP-seq") |
Janazuczkova (talk | contribs) |
||
| Line 2: | Line 2: | ||
|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-seq, ChIP-on-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
|
Application data |
|
| 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'.
[ edit box ]
Search for "ChIPmeta" in the SEQanswers forum / BioStar or:
| Web Search | Wiki Sites | Scientific |
|---|---|---|
