To facilitate the interpretation of functional genomics, epigenomics and genomics data we have developed a R-software package ReMapEnrich to identify significantly enriched regions from user defined catalogues. ReMapEnrich provide functions to import any in-house catalogue, automate and plot the enrichment analysis for genomic regions.

Need high througput ?

Then use our ChIP-seq peak enrichment analysis tool with ReMap catalogue or any other catalogues as a standalone R-package.

Genomic regions as input

Your query file containing genomic regions (eg: peaks) for which you search for enrichment. (BED format)

A catalogue of genomic elements

This will contain the catalogue of you own genomic regions (eg: regulatory). You can also use the ReMap catalogue. It has to be in BED format

A universe

(optional) You limit the enrichement to regions present in a selected universe. You can use your own genomic universe such as promoters, DNAse-seq, or ATAC-seq. (BED format)

ReMapEnrich is available as a R package on Github, view the project on :
https://github.com/remap-cisreg/ReMapEnrich

Cite us

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)

Read the docs

Please read Basic use and Advanced use for full documentations of ReMapEnrich functionalities.

Load the ReMapEnrich library

library(ReMapEnrich) 
Load the example dataset
query <- bedToGranges(system.file("extdata",
                                  "ReMap_nrPeaks_public_chr22_SOX2.bed",
                                  package = "ReMapEnrich"))

Load the ReMap catalogue

# Create a local directory 
demo.dir <- "~/ReMapEnrich_demo"
dir.create(demo.dir, showWarnings = FALSE, recursive = TRUE)

# Use the function DowloadRemapCatalog
remapCatalog2018hg38 <- downloadRemapCatalog(demo.dir)

# Load the ReMap catalogue and convert it to Genomic Ranges
remapCatalog <- bedToGranges(remapCatalog2018hg38)   

Compute enrichment

The basic way to compute an enrichment is to run with default parameters. - no universe - single core - Default shuffling - defautl overlaps. Please read Basic use vignette for more documentations

enrichment.df <- enrichment(query, remapCatalog, byChrom = TRUE)
# The option byChrom is set to TRUE as we are only working on 
# one chromosome for this analysis.