ConsTADs

Topologically associating domains (TADs) have emerged as basic structural and functional units of genome organization, and have been determined by many computational methods from Hi-C contact maps. However, the TADs obtained by different methods vary greatly, which makes the accurate determination of TADs a challenging issue and hinders subsequent biological analyses about their organization and functions. This project is about comparing different TAD-calling methods, building the TAD separation landscape and finding the consensus TADs from results of multiple methods.

Overview

This computational framework consists of three main steps, including:

1. Running different TAD-calling methods on the same Hi-C contact map;
2. Collecting the TAD boundaries identified by each method and performing boundary voting;
3. Refining the boundary score profile based on the contrast P-values of chromatin interactions using three operations, Add, Filter and Combine, to construct the TAD separation landscape.

The TAD separation landscape can be used in scenarios such as:

1. Comparing domain boundaries across multiple cell types for discovering conserved and divergent topological structures;
2. Deciphering three types of boundary regions with diverse biological features;
3. Identify Consensus Topological Associating Domains (ConsTADs).

Getting start

Installation

It's recommended to create a conda environment:

conda create -n ConsTADs python=3.7
conda activate ConsTADs

Install required packages:

pip install -r requirements.txt

Install ConsTADs by PyPI:

pip install ConsTADs

Install from source code:

git clone https://github.com/zhanglabtools/ConsTADs.git
cd ConsTADs
python setup.py build
python setup.py install

Example usage

See ConsTADs usage.ipynb.

Support

If you are having issues, please let us know. We have a mailing list located at:

• dangdachang@163.com

Citation

If ConsTADs is useful for your research, consider citing our preprint:

Defining the separation landscape of topological domains for decoding consensus domain organization of 3D genome. Dachang Dang, Shao-Wu Zhang, Ran Duan, Shihua Zhang. bioRxiv 2022.08.08.503155; doi: https://doi.org/10.1101/2022.08.08.503155