ArticleBioinformatics (Oxford, England)2022
PHi-C2: interpreting Hi-C data as the dynamic 3D genome state.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Integrative modeling of the genome structure and dynamics in fission yeast.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- The challenge of chromatin model comparison and validation: A project from the first international 4D Nucleome Hackathon.PLoS computational biology · 2025Article
- Distinct structural and functional heterochromatin partitioning of lamin B1 and lamin B2 revealed using genome-wide nicking enzyme epitope targeted DNA sequencing.Nucleic acids research · 2025Article
- 4D Genome Analysis Using PHi-C2.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Construction of Coarse-Grained Molecular Dynamics Model of Nuclear Global Chromosomes Dynamics in Mammalian Cells.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Transcription-coupled changes in genomic region proximities during transcriptional bursting.Science advances · 2024Article
- Advancements and future directions in single-cell Hi-C based 3D chromatin modeling.Computational and structural biotechnology journal · 2024Review
- Hi-C-guided many-polymer model to decipher 3D genome organization.Biophysical journal · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
summaryHigh-throughput chromosome conformation capture (Hi-C) is a widely used assay for studying the three-dimensional (3D) genome organization across the whole genome. Here, we present PHi-C2, a Python package supported by mathematical and biophysical polymer modeling that converts input Hi-C matrix data into the polymer model's dynamics, structural conformations and rheological features. The updated optimization algorithm for regenerating a highly similar Hi-C matrix provides a fast and accurate optimal solution compared to the previous version by eliminating the factors underlying the inefficiency of the optimization algorithm in the iterative optimization process. In addition, we have enabled a Google Colab workflow to run the algorithm, wherein users can easily change the parameters and check the results in the notebook. Overall, PHi-C2 represents a valuable tool for mining the dynamic 3D genome state embedded in Hi-C data. AVAILABILITY AND IMPLEMENTATION: PHi-C2 as the phic Python package is freely available under the GPL license and can be installed from the Python package index. The source code is available from GitHub at https://github.com/soyashinkai/PHi-C2. Moreover, users do not have to prepare a Python environment because PHi-C2 can run on Google Colab (https://bit.ly/3rlptGI). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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