CENTRE paper version - Short description

CENTRE is a machine learning framework that predicts enhancer target interactions in a cell-type-specific manner, using only gene expression and ChIP-seq data for three histone modifications for the cell type of interest. CENTRE uses various available datasets and extracts cell-type agnostic statistics to complement the cell-type specific information.

This repository holds the version of CENTRE published in the article under the citation section. For the development version, please check: https://github.com/slrvv/CENTRE

Contact

• Sara Lopez lopez_s@molgen.mpg.de
• Trisevgeni Rapakoulia rapakoul@molgen.mpg.de

Citation

Rapakoulia, T., Lopez Ruiz De Vargas, S., Omgba, P. A., Laupert, V., Ulitsky, I., & Vingron, M. (2023). CENTRE: A gradient boosting algorithm for Cell-type-specific ENhancer-Target pREdiction. Bioinformatics, 39(11), btad687. https://doi.org/10.1093/bioinformatics/btad687

Requirements

• R (tested 4.0.0)
• crupR
• GenomicRanges and IRanges
• metapod
• RSQLite
• xgboost

User Provided Data

CENTRE computes its features for classification based on user provided Histone ChIP-seq (H3K27ac, H3K4me3 and H3K4me1 ) and RNA-seq data for the cell-type of inetrest. As a dataframe with the genes of interest or the genes and enhancer pairs of interest.

User data :

• Cell-type specific histone ChIP-seq in BAM format for H3K27ac, H3K4me3 and H3K4me1. Additionally, a Control ChIP-seq experiment to match the HM ChIP-seq is strongly advised but CENTRE can also run without it.
• Cell -type specific RNA-seq TPM values for all genes. This dataframe will have three columns one with the ENSEMBL ID's, transcript ID's and one with the TPM values for all genes.
• A dataframe with either the GENCODE ID's for the genes of interest or enhancer (cCREs-ELS) target (GENCODE ID's) pairs of interest.

CENTRE Generic Information

CENTRE uses precomputed datasets that the user needs to download either by using the CENTRE::downloadPredcomputedData() or downloading the data from http://owww.molgen.mpg.de/~CENTRE_data/PrecomputedData.db and adding it to the /inst/extdata folder.

PrecomputedData.db is a database containing precomputed Wilcoxon rank sum tests on the following data sets:

• CAGE-seq dataset (Andersson et al.,2014)
• DNAse hypersensitivity dataset (Thurman et al.,2012)
• DNAse-seq gene expression dataset (Sheffield et al.,2013)
• CRUP-EP gene expression dataset
• Pearson Correlation between CRUP-EP(Enhancer Probability) and CRUP-PP (Promoter Probability) across 104 cell types

Installing CENTRE

#Install the development version of crupR
if (!require("devtools", quietly = TRUE))
    install.packages("devtools")
devtools::install_git("https://github.com/akbariomgba/crupR.git")
#Say yes to installing all required dependencies

#Install the development version of CENTRE
devtools::install_git("https://github.com/slrvv/CENTRE_paper_version.git")
#Say yes to installing all required dependencies

Note: If the installation of any of the dependencies of CENTRE fails try running the script CENTRE/install/install_CENTRE.R

References

• Andersson,R. et al. (2014) An atlas of active enhancers across human cell types and tissues. Nature, 507, 455–461.
• Thurman,R.E. et al. (2012) The accessible chromatin landscape of the human genome. Nature, 489, 75–82.
• Sheffield,N.C. et al. (2013) Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions. Genome Res., 23, 777–788.