ReviewNucleus (Austin, Tex.)
The statistical-mechanics of chromosome conformation capture.
Review in Nucleus (Austin, Tex.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- CAD-C: An engineered nuclease enables repair-freebioRxiv : the preprint server for biology · 2026Article
- Recurrence plot reconstruction reveals chromosomal reorganization before territory formation.NAR genomics and bioinformatics · 2026Article
- Understanding the physical processes behind DNA-DNA proximity ligation assays.Research square · 2025Article
- Understanding the physical processes behind DNA-DNA proximity ligation assays.bioRxiv : the preprint server for biology · 2025Article
- Genome architecture mapping detects transcriptionally active, multiway chromatin contacts.Nature methods · 2023Article
- Multiplex-GAM: genome-wide identification of chromatin contacts yields insights overlooked by Hi-C.Nature methods · 2023Article
- Long range inter-chromosomal interaction of Oct4 distal enhancer loci regulates ESCs pluripotency.Cell death discovery · 2023Article
- Cell type-specific chromatin topology and gene regulation.Trends in genetics : TIG · 2022Article
- Integrating Multimorbidity into a Whole-Body Understanding of Disease Using Spatial Genomics.Results and problems in cell differentiation · 2022Article
- Single-cell measurement of higher-order 3D genome organization with scSPRITE.Nature biotechnology · 2022Article
- SpectralTAD: an R package for defining a hierarchy of topologically associated domains using spectral clustering.BMC bioinformatics · 2020Article
- Evolving methodologies and concepts in 4D nucleome research.Current opinion in cell biology · 2020Review
- Bayesian inference of chromatin structure ensembles from population-averaged contact data.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Methods for mapping 3D chromosome architecture.Nature reviews. Genetics · 2020Review
- multiHiCcompare: joint normalization and comparative analysis of complex Hi-C experiments.Bioinformatics (Oxford, England) · 2019Article
- Selfish: discovery of differential chromatin interactions via a self-similarity measure.Bioinformatics (Oxford, England) · 2019Article
- R Tutorial: Detection of Differentially Interacting Chromatin Regions From Multiple Hi-C Datasets.Current protocols in bioinformatics · 2019Article
- Disentangling chromatin architecture to gain insights into the etiology of brain disorders.Current opinion in genetics & development · 2019Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since Jacob and Monod's characterization of the role of DNA elements in gene control, it has been recognized that the linear organization of genome structure is important for the regulation of gene transcription and hence the manifestation of phenotypes. Similarly, it has long been hypothesized that the spatial organization (in three dimensions evolving through time), as part of the epigenome, makes a significant contribution to the genotype-phenotype transition. Proximity ligation assays commonly known as chromosome conformation capture (3C) and 3C based methodologies (e.g., GCC, HiC and ChIA-Pet) are increasingly being incorporated into empirical studies to investigate the role that three-dimensional genome structure plays in the regulation of phenotype. The apparent simplicity of these methodologies-crosslink chromatin, digest, dilute, ligate, detect interactions-belies the complexity of the data and the considerations that should be taken into account to ensure the generation and accurate interpretation of reliable data. Here we discuss the probabilistic nature of these methodologies and how this contributes to their endogenous limitations.
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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.