To facilitate the interpretation of functional genomics, epigenomics and genomics data we have developed a R-software package
ReMapEnrichto 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)
ReMapEnrichis available as a R package on Github, view the project on :
Cite usIf you happen to use
ReMapEnrichas 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
Read the docsPlease 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 enrichmentThe 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.