ArticleNucleic acids research2015
Inferential modeling of 3D chromatin structure.
Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed, 93 citations in OpenAlex.
- The challenge of chromatin model comparison and validation: A project from the first international 4D Nucleome Hackathon.PLoS computational biology · 2025Article
- Reconstructing 3D chromosome structures from single-cell Hi-C data with SO(3)-equivariant graph neural networks.NAR genomics and bioinformatics · 2025Article
- Cohesin and CTCF complexes mediate contacts in chromatin loops depending on nucleosome positions.Biophysical journal · 2022Article
- ParticleChromo3D: a Particle Swarm Optimization algorithm for chromosome 3D structure prediction from Hi-C data.BioData mining · 2022Article
- Uncovering the Principles of Genome Folding by 3D Chromatin Modeling.Cold Spring Harbor perspectives in biology · 2022Review
- Reconstruct high-resolution 3D genome structures for diverse cell-types using FLAMINGO.Nature communications · 2022Article
- Review
- The 3D Genome: From Structure to Function.International journal of molecular sciences · 2021Review
- Improving axial resolution in Structured Illumination Microscopy using deep learning.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021Article
- High-resolution single-cell 3D-models of chromatin ensembles during Drosophila embryogenesis.Nature communications · 2021Article
- Application of Hi-C and other omics data analysis in human cancer and cell differentiation research.Computational and structural biotechnology journal · 2021Review
- ASHIC: hierarchical Bayesian modeling of diploid chromatin contacts and structures.Nucleic acids research · 2020Article
- Computational approaches for inferring 3D conformations of chromatin from chromosome conformation capture data.Methods (San Diego, Calif.) · 2020Review
- Network-based method for regions with statistically frequent interchromosomal interactions at single-cell resolution.BMC bioinformatics · 2020Article
- GSDB: a database of 3D chromosome and genome structures reconstructed from Hi-C data.BMC molecular and cell biology · 2020Article
- Computational methods for predicting 3D genomic organization from high-resolution chromosome conformation capture data.Briefings in functional genomics · 2020Article
- 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
- Joint inference and alignment of genome structures enables characterization of compartment-independent reorganization across cell types.Epigenetics & chromatin · 2019Article
- Heterogeneous Loop Model to Infer 3D Chromosome Structures from Hi-C.Biophysical journal · 2019Article
- Chromosome territories and the global regulation of the genome.Genes, chromosomes & cancer · 2019Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
For eukaryotic cells, the biological processes involving regulatory DNA elements play an important role in cell cycle. Understanding 3D spatial arrangements of chromosomes and revealing long-range chromatin interactions are critical to decipher these biological processes. In recent years, chromosome conformation capture (3C) related techniques have been developed to measure the interaction frequencies between long-range genome loci, which have provided a great opportunity to decode the 3D organization of the genome. In this paper, we develop a new Bayesian framework to derive the 3D architecture of a chromosome from 3C-based data. By modeling each chromosome as a polymer chain, we define the conformational energy based on our current knowledge on polymer physics and use it as prior information in the Bayesian framework. We also propose an expectation-maximization (EM) based algorithm to estimate the unknown parameters of the Bayesian model and infer an ensemble of chromatin structures based on interaction frequency data. We have validated our Bayesian inference approach through cross-validation and verified the computed chromatin conformations using the geometric constraints derived from fluorescence in situ hybridization (FISH) experiments. We have further confirmed the inferred chromatin structures using the known genetic interactions derived from other studies in the literature. Our test results have indicated that our Bayesian framework can compute an accurate ensemble of 3D chromatin conformations that best interpret the distance constraints derived from 3C-based data and also agree with other sources of geometric constraints derived from experimental evidence in the previous studies. The source code of our approach can be found in https://github.com/wangsy11/InfMod3DGen.
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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.