BINDER

Here, we present BINDER for accurately and robustly identifying hierarchical TADs from Hi-C data. Based on the hypothesis that the anchoring of TAD boundaries is a key feature of TADs, BINDER comprehensively generates consensus TAD boundaries and yields hierarchical TADs.

The workflow of BINDER is as follows.

Requirements for installation

1. Python 3.10.2

2. torch 2.2.2

3. numpy 1.23.5

4. scipy 1.11.0

Installation

1. Unzip "BINDER-master.zip" you have downloaded:

$ unzip BINDER-master.zip -d BINDER

2. Then enter the BINDER folder：

$ cd BINDER

3. Add execution permission for necessary program 'Infomap':

$ chmod +x Infomap

Usage of BINDER

python BINDER.py [options] -m <Hi-C matrix> -r <resolution> -chr <chromosome>

Required

--matrix/-m <string>          : Path to N x N raw Hi-C matrix;

--resolution/-r <int>         : resolution of Hi-C matrix (kb);

--chromosome/-chr <string>    : chromosome of Hi-C matrix;

Optional

--output/-o <string>          : Output folder, the output result is saved in the BINDER_result folder by default;

--normalization/-n <string>   : normalization method: 'SCN' [1], 'ICE' [2], 'KR' [3], 'sqrtVC' [1], default='SCN';

Typical commands

The following command is an example:

python BINDER.py -m example_data/GM12878_50kb_chr22.txt -r 50 -chr 22

Output

(i) The result is saved in BINDER_result/Result.chr* by default.

(ii) An example output is shown below:

Left_position	Right_position	Level	Type
17600000	18050000	3	domain
17600000	18250000	2	domain
17600000	18350000	1	domain

The first and second columns indicate the left and right positions (bp) of TAD, the third column indicates the hierarchy of TAD (level of gaps is "non-level"), and the fourth column indicates whether the base pair interval is domain or gap.

Changelog

The current version of BINDER is 1.0, subsequnt version will be updated.

References

1. SCN/sqrtVC: Zhang, S., Krieger, J. M., Zhang, Y., Kaya, C., Kaynak, B., Mikulska-Ruminska, K., Doruker, P., Li, H., & Bahar, I. (2021). ProDy 2.0: increased scale and scope after 10 years of protein dynamics modelling with Python. Bioinformatics (Oxford, England), 37(20), 3657–3659. https://doi.org/10.1093/bioinformatics/btab187

2. ICE: Servant, N., Varoquaux, N., Lajoie, B. R., Viara, E., Chen, C. J., Vert, J. P., Heard, E., Dekker, J., & Barillot, E. (2015). HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome biology, 16, 259. https://doi.org/10.1186/s13059-015-0831-x

3. KR：Kumar, R., Sobhy, H., Stenberg, P., & Lizana, L. (2017). Genome contact map explorer: a platform for the comparison, interactive visualization and analysis of genome contact maps. Nucleic acids research, 45(17), e152. https://doi.org/10.1093/nar/gkx644