Bioinformatics tools to compute statistical enrichment of geonomic regions within ReMap catalogue or any other catalogue of peaks.
Current next generation sequencing studies generate a large variety of genomic regions ranging from regulatory regions with transcription factors or histone marks ChIP-seq to variant calls or coding/non-coding transcripts. Also, the number of complex catalogues from large-scale integrative efforts are increasing and large sequencing projects. 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.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
Here are dependencies used in our package. Build will prompt for installation.
R.utils, data.table, RMySQL, GenomicRanges
Install the ReMapEnrich R-package from GitHub
To install a R package, start by installing the
devtools package from CRAN.
Install the ReMapEnrich R-package from GitHub using the following code, where you need to remember to list both the author and the name of the package
Installing GitHub packages into RStudio
Although RStudio does have various tools for installing packages, the most straightforward approach is to download the zipped file, open the
ReMapEnrich.Rpoject file, and in Rstudio Build -> Install and Restart.
Or just follow the steps described in the previous section, entering the code into the Console in RStudio.
This example is based on small dataset (input and catalog) released with the ReMapEnrich package. It will go through various steps : loading data, computing enrichments, visualizing results.
Please read Basic use documentations, and plot examples.
Here we will be discovering more advanced functions and possibilities of the ReMapEnrich package. You may want to read the basics functions first in order to understand the principles of enrichment analysis.
Please read Advanced use documentations, and universe usage.
Quick Enrichement Code - no fuzz
Load the ReMapEnrich library
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)
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.
Plot the enriched TFs
- Zacharie Menetrier - ChienBleu
- Martin Mestdagh - MartinMestdagh
- Jacques van Helden - jvanheld
- Laurent Tichit - Laulo
- Benoit Ballester - benoitballester
This project is licensed under the MIT License - see the LICENSE.md file for details
We are grateful to Aurélien Griffon and Quentin Barbier for their very early contributions to this project.