Osuntoki, Itunu G and Harrison, Andrew and Dai, Hongsheng and Bao, Yanchun and Zabet, Nicolae Radu (2022) ZipHiC: a novel Bayesian framework to identify enriched interactions and experimental biases in Hi-C data. Bioinformatics, 38 (14). btac387-btac387. DOI https://doi.org/10.1093/bioinformatics/btac387
Osuntoki, Itunu G and Harrison, Andrew and Dai, Hongsheng and Bao, Yanchun and Zabet, Nicolae Radu (2022) ZipHiC: a novel Bayesian framework to identify enriched interactions and experimental biases in Hi-C data. Bioinformatics, 38 (14). btac387-btac387. DOI https://doi.org/10.1093/bioinformatics/btac387
Osuntoki, Itunu G and Harrison, Andrew and Dai, Hongsheng and Bao, Yanchun and Zabet, Nicolae Radu (2022) ZipHiC: a novel Bayesian framework to identify enriched interactions and experimental biases in Hi-C data. Bioinformatics, 38 (14). btac387-btac387. DOI https://doi.org/10.1093/bioinformatics/btac387
Abstract
Motivation Several computational and statistical methods have been developed to analyse data generated through the 3C-based methods, especially the Hi-C. Most of the existing methods do not account for dependency in Hi-C data. Results Here, we present ZipHiC, a novel statistical method to explore Hi-C data focusing on the detection of enriched contacts. ZipHiC implements a Bayesian method based on a hidden Markov random field (HMRF) model and the Approximate Bayesian Computation (ABC) to detect interactions in two-dimensional space based on a Hi-C contact frequency matrix. ZipHiC uses data on the sources of biases related to the contact frequency matrix, allows borrowing information from neighbours using the Potts model and improves computation speed by using the ABC model. In addition to outperforming existing tools on both simulated and real data, our model also provides insights into different sources of biases that affects Hi-C data. We show that some datasets display higher biases from DNA accessibility or Transposable Elements content. Furthermore, our analysis in D. melanogaster showed that approximately half of the detected significant interactions connect promoters with other parts of the genome indicating a functional biological role. Finally, we found that the micro-C datasets display higher biases from DNA accessibility compared to a similar Hi-C experiment, but this can be corrected by ZipHiC.
Item Type: | Article |
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Uncontrolled Keywords: | Chromatin; Animals; Drosophila melanogaster; DNA; Bayes Theorem; Software; Bias |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Life Sciences, School of Faculty of Science and Health > Mathematics, Statistics and Actuarial Science, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 13 Jul 2022 11:55 |
Last Modified: | 30 Oct 2024 19:34 |
URI: | http://repository.essex.ac.uk/id/eprint/33147 |
Available files
Filename: btac387.pdf
Licence: Creative Commons: Attribution 3.0