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Alinejad-Rokny H, Ghavami Modegh R, Rabiee HR, Ramezani Sarbandi E, Rezaie N, Tam KT, Forrest ARR. Correction: MaxHiC: A robust background correction model to identify biologically relevant chromatin interactions in Hi-C and capture Hi-C experiments. PLoS Comput Biol 2022; 18:e1010515. [PMID: 36083878 PMCID: PMC9462553 DOI: 10.1371/journal.pcbi.1010515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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Alinejad-Rokny H, Ghavami Modegh R, Rabiee HR, Ramezani Sarbandi E, Rezaie N, Tam KT, Forrest ARR. MaxHiC: A robust background correction model to identify biologically relevant chromatin interactions in Hi-C and capture Hi-C experiments. PLoS Comput Biol 2022; 18:e1010241. [PMID: 35749574 PMCID: PMC9262194 DOI: 10.1371/journal.pcbi.1010241] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 07/07/2022] [Accepted: 05/23/2022] [Indexed: 12/13/2022] Open
Abstract
Hi-C is a genome-wide chromosome conformation capture technology that detects interactions between pairs of genomic regions and exploits higher order chromatin structures. Conceptually Hi-C data counts interaction frequencies between every position in the genome and every other position. Biologically functional interactions are expected to occur more frequently than transient background and artefactual interactions. To identify biologically relevant interactions, several background models that take biases such as distance, GC content and mappability into account have been proposed. Here we introduce MaxHiC, a background correction tool that deals with these complex biases and robustly identifies statistically significant interactions in both Hi-C and capture Hi-C experiments. MaxHiC uses a negative binomial distribution model and a maximum likelihood technique to correct biases in both Hi-C and capture Hi-C libraries. We systematically benchmark MaxHiC against major Hi-C background correction tools including Hi-C significant interaction callers (SIC) and Hi-C loop callers using published Hi-C, capture Hi-C, and Micro-C datasets. Our results demonstrate that 1) Interacting regions identified by MaxHiC have significantly greater levels of overlap with known regulatory features (e.g. active chromatin histone marks, CTCF binding sites, DNase sensitivity) and also disease-associated genome-wide association SNPs than those identified by currently existing models, 2) the pairs of interacting regions are more likely to be linked by eQTL pairs and 3) more likely to link known regulatory features including known functional enhancer-promoter pairs validated by CRISPRi than any of the existing methods. We also demonstrate that interactions between different genomic region types have distinct distance distributions only revealed by MaxHiC. MaxHiC is publicly available as a python package for the analysis of Hi-C, capture Hi-C and Micro-C data.
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Affiliation(s)
- Hamid Alinejad-Rokny
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Perth, Australia
- Bio Medical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, Australia
- Health Data Analytics Program, AI-enabled Processes (AIP) Research Centre, Macquarie University, Sydney, Australia
- * E-mail: (HAR); (ARRF)
| | - Rassa Ghavami Modegh
- Bioinformatics and Computational Biology Lab, Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Hamid R. Rabiee
- Bioinformatics and Computational Biology Lab, Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Ehsan Ramezani Sarbandi
- Bioinformatics and Computational Biology Lab, Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Narges Rezaie
- Center for Complex Biological Systems, University of California Irvine, Irvine, California, United States of America
| | - Kin Tung Tam
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Perth, Australia
| | - Alistair R. R. Forrest
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Perth, Australia
- * E-mail: (HAR); (ARRF)
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