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.
This computational framework consists of three main steps, including:
- Running different TAD-calling methods on the same Hi-C contact map;
- Collecting the TAD boundaries identified by each method and performing boundary voting;
- 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:
- Comparing domain boundaries across multiple cell types for discovering conserved and divergent topological structures;
- Deciphering three types of boundary regions with diverse biological features;
- Identify Consensus Topological Associating Domains (ConsTADs).
We also give the results of ConsTADs for GM12878 as an example here.
It's recommended to create a conda environment:
conda create -n ConsTADs python=3.7
conda activate ConsTADsDownload packages
git clone https://github.com/zhanglabtools/ConsTADs.git
cd ConsTADsInstall required packages:
pip install -r requirement.txtInstall ConsTADs by PyPI:
pip install ConsTADsInstall from source code:
python setup.py build
python setup.py installSee ConsTADs usage.ipynb.
If you are having issues, please let us know. We have a mailing list located at:
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. Genome Res., 2023, doi: 10.1101/gr.277187.122.