ECLIPSE (Enhancer Calling and Linking with Integrated Profiling and Structure Evaluation) provides a performant implementation of the rank ordering of super enhancers (ROSE) method for identifying super enhancers. It provides options to increase the robustness of ROSE via signal transformations prior to thresholding and additional thresholding approaches. It also increases flexibility by exposing parameters hidden in the original implementation. ECLIPSE additionally contains novel functionality to identify sub-structural changes within enhancer regions between groups via sliding window and binning approaches. It also contains visualization functions to generate high-quality plots of specific loci alongside arbitrary user-provided data.
This project was conceptualized for and initially developed during the St. Jude Children's Research Hospital KIDS24 BioHackathon by:
- Jared Andrews (team lead)
- Alyssa Obermayer
- Nicolas Peterson
- Kelly McCastlain
- Jacqueline Myers
- Avery Bradley
It even snagged a prize for "Most Technically Impressive" project.
This package is under active development and may break at any time.
Currently, the package can be installed from GitHub:
library(devtools)
devtools::install_github("stjude-biohackathon/ECLIPSE")
Given paths to a BAM file and a BED file of peaks, ROSE can be run with the run_rose function.
Optionally, a control BAM file for input or IgG from the same sample can be provided.
Alternatively, bamFile objects for the treatment and control signal and a GRanges object for the peaks can be provided.
The output is a GRanges object containing all putative enhancers with their super enhancer designation in the super metadata column.
Below is an example of running ROSE on a BAM file of H3K27ac MINT ChIP-seq, an input control BAM file, and a BED file of peaks from this ENCODE experiment of human naive B cells.
See ?run_rose for more details.
# We'll use the BiocFileCache package to download and cache the files, which will take a few minutes the first time they're used.
library(BiocFileCache)
# For annotation
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(org.Hs.eg.db)
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
org.db <- org.Hs.eg.db
bfc <- BiocFileCache(ask = FALSE)
treat_url <- "https://www.encodeproject.org/files/ENCFF993DJI/@@download/ENCFF993DJI.bam"
treat_path <- bfcrpath(bfc, treat_url)
control_url <- "https://www.encodeproject.org/files/ENCFF821MAI/@@download/ENCFF821MAI.bam"
control_path <- bfcrpath(bfc, control_url)
peaks_url <- "https://www.encodeproject.org/files/ENCFF590DFY/@@download/ENCFF590DFY.bed.gz"
peaks_path <- bfcrpath(bfc, peaks_url)
naiveB1_enhancers <- run_rose(treatment = treat_path, control = control_path, peaks = peaks_path,
txdb = txdb, org.db = org.db, stitch.distance = 12500, tss.exclusion.distance = 2500,
max.unique.gene.tss.overlap = 2)
naiveB1_enhancersAdd missing ROSE functionality.TSS exclusion from stitching process.- ~~Overlap of TSS of 3 or more unique genes canceling stiching for a putative enhancer. ~~
And ability to disable this process.
Enhancer-gene annotations (within 50 kb by default for ROSE).Ability to limit to expressed genes.
- Allow BigWig or GRanges signal as input.
If you use this package in published research, please cite the original papers utilizing and describing ROSE:
Master Transcription Factors and Mediator Establish Super-Enhancers at Key Cell Identity Genes Warren A. Whyte, David A. Orlando, Denes Hnisz, Brian J. Abraham, Charles Y. Lin, Michael H. Kagey, Peter B. Rahl, Tong Ihn Lee and Richard A. Young Cell 153, 307-319, April 11, 2013
Selective Inhibition of Tumor Oncogenes by Disruption of Super-enhancers Jakob Lovén, Heather A. Hoke, Charles Y. Lin, Ashley Lau, David A. Orlando, Christopher R. Vakoc, James E. Bradner, Tong Ihn Lee, and Richard A. Young Cell 153, 320-334, April 11, 2013