HiCPotts: An R/Bioconductor package to identify significant interactions in chromosome conformation capture data and model sources of bias

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ID: 328223
2026
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Abstract
MOTIVATION: Chromosome Conformation Capture methods, including Hi-C, micro-C or Capture-C, are used to map chromatin interactions genome-wide. Most of the existing computational methods do not account for sources of bias (such as DNA accessibility, GC content or TE content) in the data. RESULTS: We previously developed ZipHiC, a Bayesian method based on the hidden Markov random field (HMRF) model and the Approximate Bayesian Computation (ABC), that uses zero-inflated Poisson distribution to model the noise, signal and false signal of the data and showed that this approach was able to detect bias from DNA accessibility, GC content and TE content in both Hi-C and micro-C data. Here, we present HiCPotts, another Bayesian method based on the HMRF model and the ABC that uses a zero-inflated Negative Binomial distribution instead to model the noise and signal of the data. We systematically show that HiCPotts reduces false positives and increases recovery of true interactions compared to ZipHiC, but also compared to other methods such as FastHiC, Juicer and HiCExplorer. Most importantly, we provide an R/Bioconductor package that allows modelling the noise, signal and false signal using various distributions such as the zero-inflated Negative Binomial (ZINB) and the zero-inflated Poisson distribution (ZIP). AVAILABILITY AND IMPLEMENTATION: https://bioconductor.org/packages/HiCPotts/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7212143764 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Itunu Godwin Osuntoki, Andrew Harrison, Hongsheng Dai, Yanchun Bao, Nicolae Radu Zabet
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag673
URL
https://doi.org/10.1093/bioinformatics/btag673
Keywords Keywords not found

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