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epiRomics: a multi-omics R package to identify and visualize enhancers

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An R package designed to integrate and visualize various levels of epigenomic information, including but not limited to: ChIP, Histone, ATAC, and RNA sequencing. epiRomics is also designed for regulatory network analysis in order to identify enhancer and enhanceosome regions from these data.

Please contact for suggestions, feedback, or bug reporting.

Interactive Showcase of Package

Explore epiRomics results interactively through our companion Shiny web applications:

Shiny epiRomics Mouse Alpha, Beta, and Delta Cells Browser

Shiny epiRomics Mouse Islet Enhanceosome Browser — Interactive browser exploring mouse pancreatic islet enhancers and enhanceosome analysis results from our 2023 BMC Genomics publication.

Paper: Alex M. Mawla, Talitha van der Meulen, Mark O. Huising. (2023). Chromatin accessibility differences between alpha, beta, and delta cells identifies common and cell type-specific enhancers. BMC Genomics. 10.1186/s12864-023-09293-6

Shiny epiRomics Human Alpha and Beta Cells

Shiny epiRomics Human Islet Enhanceosome Browser — Interactive browser exploring human pancreatic islet enhancer and enhanceosome analysis results used to create the example data and vignette for the epiRomics Package.

Package: Alex M. Mawla & Mark O. Huising. (2021). epiRomics: a multi-omics R package to identify and visualize enhancers. bioRxiv 2021.08.19.456732. 10.1101/2021.08.19.456732

Features

  • Multi-omics Integration: Seamlessly integrate ChIP-seq, ATAC-seq, RNA-seq, and histone modification data
  • Enhancer Identification: Identify putative enhancer regions using histone mark combinations
  • Enhanceosome Analysis: Detect enhanceosome regions through co-transcription factor analysis
  • Memory-Efficient Processing: Built-in BigWig caching for large-scale data analysis
  • Comprehensive Visualization: Generate publication-ready genomic tracks and plots
  • Robust Error Handling: Comprehensive parameter validation and error messages

Installation

Prerequisites

epiRomics requires R version 4.5.0 or higher and several Bioconductor packages. Make sure you have the latest version of R and Bioconductor installed.

if (!requireNamespace("BiocManager", quietly = TRUE)) {
  install.packages("BiocManager")
}
BiocManager::install("epiRomics")

library(epiRomics)

# Optional: download the full example dataset (~1.3 GB, one-time,
# cached via BiocFileCache). The package also ships a small toy
# subset in `inst/extdata/toy/` that runs all examples without any
# download.
epiRomics::cache_data()

Install from GitHub (development / not recommended for regular users)

Note: The Bioconductor release channel is the supported install path. GitHub installation is intended for developers tracking the main branch between releases and is not advised for regular users.

if (!requireNamespace("BiocManager", quietly = TRUE)) {
  install.packages("BiocManager")
}
BiocManager::install("Huising-Lab/epiRomics")

Quick Start 

# Load the package
library(epiRomics)

# Build epiRomics database
database <- build_database(
  db_file = "path/to/your/data.csv",
  txdb_organism = "TxDb.Hsapiens.UCSC.hg38.knownGene",
  genome = "hg38",
  organism = "org.Hs.eg.db"
)

# Identify putative enhancers
enhancers <- find_enhancers_by_comarks(
  database = database,
  histone_mark_1 = "h3k4me1",
  histone_mark_2 = "h3k27ac"
)

# Identify enhanceosomes
enhanceosomes <- find_enhanceosomes(
  putative_enhancers = enhancers,
  database = database
)

# Visualize results
plot_tracks(
  enhanceosomes,
  index = 1,
  database = database,
  track_connection = your_track_data
)

Documentation

Example Data

Example data is downloaded on demand via cache_data() (~1.3 GB, cached locally after first download). The dataset focuses on delineating putative human pancreatic islet enhancers between alpha and beta cells and includes:

  • Human pancreatic islet alpha and beta ATAC-seq data from GEO accession GSE76268
  • ChIP-seq data for transcription factors Foxa2, MafB, Nkx2.2, Nkx6.1, and Pdx1
  • Histone modification data (H3K27ac, H3K4me1,H3K27me3, H3K9me3, H3K4me3, H3K36me3, H2A.Z)
  • FANTOM5 human enhancer database
  • Human ultra-conserved non-coding elements (UCNEs)

Citation

If you use epiRomics, please cite:

Alex M. Mawla, Talitha van der Meulen, Mark O. Huising. (2023). Chromatin accessibility differences between alpha, beta, and delta cells identifies common and cell type-specific enhancers. BMC Genomics. 10.1186/s12864-023-09293-6

Alex M. Mawla & Mark O. Huising. (2021). epiRomics: a multi-omics R package to identify and visualize enhancers. bioRxiv 2021.08.19.456732. 10.1101/2021.08.19.456732

Contributing

We welcome contributions! Please feel free to submit issues, feature requests, or pull requests on our GitHub repository.

License

This package is licensed under the Artistic License 2.0. See the LICENSE file for details.

Contact

For questions, suggestions, or bug reports, please contact:

Troubleshooting

Installation Issues

Problem: Package dependencies fail to install
Solution: Make sure you have the latest version of R (≥ 4.5.0) and Bioconductor:

# Update R packages
update.packages(ask = FALSE)

# Update Bioconductor
BiocManager::install(version = BiocManager::version())

# If installation fails, try installing core dependencies manually:
BiocManager::install(c("AnnotationDbi", "annotatr", "BiocGenerics",
                       "GenomeInfoDb", "GenomicFeatures", "GenomicRanges",
                       "IRanges", "org.Hs.eg.db", "rtracklayer",
                       "TxDb.Hsapiens.UCSC.hg38.knownGene", "data.table"))

Problem: Compilation errors on Linux
Solution: Install required system dependencies:

# Ubuntu/Debian
sudo apt-get update
sudo apt-get install -y libxml2-dev libcurl4-openssl-dev libssl-dev

# CentOS/RHEL  
sudo yum install libxml2-devel openssl-devel libcurl-devel