Biology subjects

Osuntoki, I. G.

Publications and source records attributed to Osuntoki, I. G..

2 recordsLinked to original sources

bioRxiv2026

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

MotivationChromosome 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 biases (such as DNA accessibility, GC content or TE content) in the data. ResultsWe previously developed ZipHiC, a Bayesian method based on a 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 biases 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). Availabilityhttps://bioconductor.org/packages/HiCPotts/

bioinformatics↗

bioRxiv2021

ZipHiC: a novel Bayesian framework to identify enriched interactions and experimental biases in Hi-C data

Several computational and statistical methods have been developed to analyse data generated through the 3C-based methods, especially the Hi-C. Most of existing methods do not account for dependency in Hi-C data. Here, we present ZipHiC, a novel statistical method to explore Hi-C data focusing on 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 Hi-C contact frequency matrix. ZipHiC uses data on the sources of biases related to 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, 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.

bioinformatics↗