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Accurate prediction of cohesin-mediated 3D genome organization from 2D chromatin features
Open accessPreprint — published as 10.1186/s13059-023-03158-7 · Paper page
Abstract
The three-dimensional (3D) genome organization influences diverse nuclear processes. Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) and Hi-C are powerful methods to study the 3D genome organization. However, ChIA-PET and Hi-C experiments are expensive, time-consuming, require tens to hundreds of millions of cells, and are challenging to optimize and analyze. Predicting ChIA-PET/Hi-C data using cheaper ChIP-Seq data and other easily obtainable features could be a useful alternative. It is well-established that the cohesin protein complex is a key determinant of 3D genome organization. Here we present Chromatin Interaction Predictor (ChIPr), a suite of regression models based on deep neural networks (DNN), random forest, and gradient boosting, respectively, to predict cohesin-mediated chromatin interaction strength between any two loci in the genome. Comprehensive tests on four cell lines show that the predictions of ChIPr correlate well with the original ChIA-PET data at the peak-level resolution and bin sizes of 25 and 5 Kbp. In addition, ChIPr can accurately capture most of the cell-type-dependent loops identified by ChIA-PET and Hi-C data. Rigorous feature testing indicated that genomic distance and RAD21 (a cohesin component) ChIP-Seq signals are the most important inputs for ChIPr in determining chromatin interaction strength. The standard ChIPr model requires three experimental inputs: ChIP-Seq signals for RAD21, H3K27ac (enhancer/active chromatin mark) and H3K27me3 (inactive chromatin mark). The minimal ChIPr model performs comparably and requires a single experimental input: ChIP-Seq signals for RAD21. Integrative analysis revealed novel insights into the role of CTCF motif, its orientation, and CTCF binding on the prevalence and strength of cohesin-mediated chromatin interactions. These studies outline the general features of genome folding and open new avenues to analyze spatial genome organization in specimens with limited cell numbers.
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Metadata
- Authors
- Abbas, A., Chandratre, K., Gao, Y., Yuan, J., Zhang, M. Q., & Mani, R. S.
- Affiliations
- The University of Texas Southwestern Medical Center (US)
- The University of Texas at Dallas (US)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Harold C. Simmons Comprehensive Cancer Center (US)
- Corresponding author
- Zhang, Michael Q.
- Mani, Ram S.
- Venue
- Genome biology
- Publisher
- openRxiv
- Published
- 2022-11-25
- Volume
- 25
- Issue
- 1
- Pages
- 15-15
- ISSN
- 1465-6906 (ISSN-L)
- PMC
- PMC10785520
- OpenAlex
- W4390790903
- Cited by
- 13
- References
- 1
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 6
- FWCI
- 1.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Genome biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Defense (W81XWH, W81XWH2110114)
- Cancer Prevention and Research Institute of Texas (RP230382)
- University of Texas Southwestern Medical Center
- National Institutes of Health (W81XWH)
- National Cancer Institute (R01CA245294)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Chromosomal and Genetic Variations
- Keywords
- 3D genome organization, RAD21, H3K27ac, H3K27me3, CTCF binding, chromatin interaction prediction, deep neural network, gradient boosting, random forest, ChIA-PET
- MeSH terms
- CCCTC-Binding Factor, Cohesins, Chromatin, Chromosomal Proteins, Non-Histone, Cell Cycle Proteins
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