TADreg: A versatile regression framework for TAD identification, differential analysis and prediction

Overview

We propose a versatile regression framework which not only identifies TADs in a fast and accurate manner, but also detects differential TAD borders across conditions for which few methods exist, and predicts 3D genome reorganization after chromosomal rearrangement. Moreover, the framework is biologically meaningful, has an intuitive interpretation and is easy to visualize.

Requirements

The scripts were written in R language.

To run the scripts, you need several R packages. To install the packages:

install.packages(c("devtools","Matrix","glmnet","data.table","ggplot2","circlize","mgcv","L0Learn","doMC"))
BiocManager::install(c("BSgenome.Mmusculus.UCSC.mm10","rtracklayer","GenomicRanges","HiTC","hicrep"))

For R > 4.0.0, the installation of "hicrep" might not work with BiocManager for some reasons. Instead, you could install "hicrep" directly from the github repository:

library(devtools)  
remotes::install_github("aidenlab/straw/R")  
devtools::install_github(“TaoYang-dev/hicrep”)

Installation

library(devtools)  
devtools::install_github("morphos30/TADreg")  

Usage

In the folder Tutorial, you will find an R Markdown file main_package.html which will explain with examples how to use TADreg R package.

Folders

In this package, there are three main folders:

• The folder "data" contains: Hi-C data matrices formatted as HiTC R objects (https://www.bioconductor.org/packages//2.10/bioc/html/HiTC.html).
• The folder "script" contains one main R script "main_package.R" for contains examples for running the three functions SIM (TAD border identification), DIM (differential TAD border detection) and PIM (prediction of rearranged 3D genome).
• The folder "results" contains three subfolders: SIM (TAD border identification), DIM (differential TAD border detection) and PIM (prediction of rearranged 3D genome). In the folders, there are already plots illustrating the results obtained from the three functions.

Importing data from standard Hi-C processing pipelines

The function ProcData(htc,distMax=2.5e6) from TADreg processes data from HTCexp object from the HiTC R package format :). https://bioconductor.org/packages/release/bioc/manuals/HiTC/man/HiTC.pdf

From Juicer

In TADreg, you will find a function to import Hi-C data from Juicer dump/draw format.

HTCfromJuicerDump(file_juicer_dump, resolution, chr, assembly, sparse=T)

The function will return a HTCexp object from HiTC R package.

From HiC-pro

HiC-Pro is compatible with HiTC R package (same author).
https://bioconductor.org/packages/release/bioc/manuals/HiTC/man/HiTC.pdf

See explanations from:
https://github.com/nservant/HiC-Pro/blob/master/doc/COMPATIBILITY.md

For instance, in R, you can type:

hic<-importC("HI-C-hrpL-KB_29000_iced.matrix","HI-C-hrpL-KB_29000_abs.bed")
names(hic@xgi)=paste0("bin",1:length(hic@xgi)) # this line will fix some bugs for DIM function

From any other tools

You can convert most Hi-C formats to Juicer format with hicConvertFormat from HiC explorer.

https://hicexplorer.readthedocs.io/en/latest/content/tools/hicConvertFormat.html

Reference

Raphaël Mourad, TADreg: a versatile regression framework for TAD identification, differential analysis and rearranged 3D genome prediction. BMC Bioinformatics. 2022 Mar 2;23(1):82. doi: 10.1186/s12859-022-04614-0. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04614-0

Contact:

raphael.mourad@univ-tlse3.fr