ReviewBriefings in bioinformatics2023
The shape of chromatin: insights from computational recognition of geometric patterns in Hi-C data.
Review in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 18 citations in OpenAlex.
- Reversing aging-like 3D genome disorganization in abioRxiv : the preprint server for biology · 2026Article
- BandHiC: a memory-efficient and user-friendly Python package for organizing and analyzing Hi-C matrices down to sub-kilobase resolution.BMC genomics · 2026Article
- Accurate and robust 3D genome feature discovery from multiplexed DNA FISH.bioRxiv : the preprint server for biology · 2025Article
- MIA-Jet: Multi-scale Identification Algorithm of Chromatin Jets.bioRxiv : the preprint server for biology · 2025Article
- StripePy: fast and robust characterization of architectural stripes.Bioinformatics (Oxford, England) · 2025Article
- Unveiling Multi-Scale Architectural Features in Single-Cell Hi-C Data Using scCAFE.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Review
- Polymer model integrates imaging and sequencing to reveal how nanoscale heterochromatin domains influence gene expression.Nature communications · 2025Article
- Mapping the 3D genome architecture.Computational and structural biotechnology journal · 2025Review
- CWL-Based Analysis Pipeline for Hi-C Data: From FASTQ Files to Matrices.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Genome structural dynamics: insights from Gaussian network analysis of Hi-C data.Briefings in functional genomics · 2024Article
- ENT3C: an entropy-based similarity measure for Hi-C and micro-C derived contact matrices.NAR genomics and bioinformatics · 2024Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The three-dimensional organization of chromatin plays a crucial role in gene regulation and cellular processes like deoxyribonucleic acid (DNA) transcription, replication and repair. Hi-C and related techniques provide detailed views of spatial proximities within the nucleus. However, data analysis is challenging partially due to a lack of well-defined, underpinning mathematical frameworks. Recently, recognizing and analyzing geometric patterns in Hi-C data has emerged as a powerful approach. This review provides a summary of algorithms for automatic recognition and analysis of geometric patterns in Hi-C data and their correspondence with chromatin structure. We classify existing algorithms on the basis of the data representation and pattern recognition paradigm they make use of. Finally, we outline some of the challenges ahead and promising future directions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.