A statistical approach for inferring the 3D structure of the genome
Open accessAbstract
MOTIVATION: Recent technological advances allow the measurement, in a single Hi-C experiment, of the frequencies of physical contacts among pairs of genomic loci at a genome-wide scale. The next challenge is to infer, from the resulting DNA-DNA contact maps, accurate 3D models of how chromosomes fold and fit into the nucleus. Many existing inference methods rely on multidimensional scaling (MDS), in which the pairwise distances of the inferred model are optimized to resemble pairwise distances derived directly from the contact counts. These approaches, however, often optimize a heuristic objective function and require strong assumptions about the biophysics of DNA to transform interaction frequencies to spatial distance, and thereby may lead to incorrect structure reconstruction. METHODS: We propose a novel approach to infer a consensus 3D structure of a genome from Hi-C data. The method incorporates a statistical model of the contact counts, assuming that the counts between two loci follow a Poisson distribution whose intensity decreases with the physical distances between the loci. The method can automatically adjust the transfer function relating the spatial distance to the Poisson intensity and infer a genome structure that best explains the observed data. RESULTS: We compare two variants of our Poisson method, with or without optimization of the transfer function, to four different MDS-based algorithms-two metric MDS methods using different stress functions, a non-metric version of MDS and ChromSDE, a recently described, advanced MDS method-on a wide range of simulated datasets. We demonstrate that the Poisson models reconstruct better structures than all MDS-based methods, particularly at low coverage and high resolution, and we highlight the importance of optimizing the transfer function. On publicly available Hi-C data from mouse embryonic stem cells, we show that the Poisson methods lead to more reproducible structures than MDS-based methods when we use data generated using different restriction enzymes, and when we reconstruct structures at different resolutions. AVAILABILITY AND IMPLEMENTATION: A Python implementation of the proposed method is available at http://cbio.ensmp.fr/pastis.
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Hi-C networks can be used to reconstruct the 3D structure of chromatin and its spatial distribution within the nucleus.
“Similar to how atomic or amino-acid distances are used in determination of protein structure, HiC networks can be used to reconstruct the 3D structure of chromatin and its spatial distribution within the nucleus (Varoquaux et al., 2014).”
From Mapping the multiscale structure of biological systems (2021) · cites this paper as Varoquaux et al., 2014
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- Authors
- Varoquaux, N., Ay, F., Noble, W. S., & Vert, J.-P.
- Affiliations
- Inserm (FR)
- Corresponding author
- Noble, William Stafford
- Vert, Jean-Philippe
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-06-11
- Volume
- 30
- Issue
- 12
- Pages
- i26-i33
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC4229903
- OpenAlex
- W2165780114
- Cited by
- 246
- References
- 31
- Citations by year
- 2026: 13 · 2025: 12 · 2024: 17 · 2023: 27 · 2022: 16 · 2021: 23 · 2020: 18 · 2019: 29 · 2018: 24 · 2017: 17 · 2016: 25 · 2015: 20 · 2014: 5
- FWCI
- 9.11
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- HAL (submittedVersion)
- Funders
- European Commission (305626)
- Agence Nationale de la Recherche (ANR-11-BINF-0001)
- National Institutes of Health (-0001, GM103533)
- National Human Genome Research Institute (U41HG007000)
- National Institute of General Medical Sciences (P41GM103533)
- National Institute of Allergy and Infectious Diseases (R01AI106775)
- FP7 Ideas: European Research Council (280032)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomic variations and chromosomal abnormalities
- Keywords
- Hi-C contact maps, chromosome folding, transfer function optimization, multidimensional scaling, mouse embryonic stem cells, reproducibility
- MeSH terms
- Algorithms, Animals, Chromosomes, DNA, Models, Molecular, Nucleic Acid Conformation, Models, Statistical, Poisson Distribution, Genome, Genomics, Mice
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