Chromatin conformation capture technologies such as Hi-C have revealed that the genome is organized in a hierarchy of structures spanning multiple scales observed at different resolutions. Current algorithms often focus on specific interaction patterns found at a specific Hi-C resolution. We present BHi-Cect 2.0, a method that leverages Hi-C data at multiple resolutions to describe chromosome architecture as nested preferentially self-interacting clusters using spectral clustering. This new version describes the hierarchical configuration of chromosomes by now integrating multiple Hi-C data resolutions. Our new implementation offers a more comprehensive description of the multi-scale architecture of the chromosomes. We further provide these functionalities as an R package to assist their integration with other computational pipelines. The BHiCect 2.0 R packages is available on github at https://github.com/princeps091-binf/BHiCect2with the version used for this manuscript on Zenodo at https://doi.org/10.5281/zenodo.17985844.
@article{arxiv.2512.17512,
title = {BHiCect 2.0: Multi-resolution clustering of Hi-C data},
author = {Vipin Kumar and Roberto Rossini and Jonas Paulsen and Anthony Mathelier},
journal= {arXiv preprint arXiv:2512.17512},
year = {2025}
}