Cross your genomic regions against the ReMap catalogue of transcription factor binding peaks. You can annotate your BED file and calculate statistical enrichments of TFs within your regions based on their binding locations.
(1) You need to input a query file containing genomic regions (eg: peaks) for which you search for enrichment (BED format). (2) Select the ReMap catalogue of genomic elements. (3) Click RUN
Please load your BED file (<25Mo). Make sure your it is correctly formated. See UCSC BED format for more details
You can adjust here how your input or the catalog overlap, as well as the number of random.
Limit the enrichement to regions present in a selected universe (optional). You can upload your universe or choose from a selection of genomic universe. This will significantly increase compute time
Limit the enrichement to regions present in a selected universe (optional).
1. Category : regulator identifier showing significant overlap with the query.
2. Nb of overlap : nb of overlaps between the query and each categories of the catalogue.
3. Random average : mean number of overlaps between all the shuffles and the catalogue.
4. Mapped peaks ratio : percentage of coverage of each categories of the catalogue by the query.
5. Effect size : log ratio between the observed and expected number of overlaps.
6. P-significance : minus-logarithm of the p-value: $p_{sig} = -log_{10}(p)$, enabling to highlight the relevant order of magnitude on graphical representations (e.g. volcano plot).
7. P-value : probability to observe an effect at least as extreme as the result, under null hypothesis, which can be interpreted as an estimation of the false positive rate (FPR). The p-value is computed with the Poisson distribution (and validated empirically with randomized query regions).
8. Q-significance : minus-logarithm of the q-value: $q_{sig} = -log_{10}(q)$
9. Q-value : correction of the p-value for multiple testing (due to the fact that the query is compared to each regulator of the remap catalogue). The q-value is an estimation of the false discovery rate (FDR). Several multiple-testing correction methods are supported by ReMapEnrich (default: "BH" for Benjamini-Hochberg).
10. E-significance : minus-logarithm of the e-value
11. E-value: expected number of false positives for a given p-value.
Use ChIP-seq peak enrichment analysis with ReMap catalogue or any other catalogue as a standalone package.
ReMapEnrich
is available as a R package on Github, view the Project on :
https://github.com/remap-cisreg/ReMapEnrich
If you happen to use ReMapEnrich as R-package, please cite:
Ménétrier Z., Mestdagh M., et al, in prep
If you happen to use ReMapEnrich-Web interface, please cite:
ReMap2020 in prep
1. First you need to install the devtools package
install.packages("devtools")
2. Then, load the devtools package, and install the Github package
library(devtools)
install_github("remap-cisreg/ReMapEnrich")
3. Load our library and follow the Github instructions
library(ReMapEnrich)