ASHIC: Hierarchical Bayesian modeling of diploid chromatin contacts and structures

Abstract

The recently developed Hi-C technique has been widely applied to map genome-wide chromatin interactions. However, current methods for analyzing diploid Hi-C data cannot fully distinguish between homologous chromosomes. Consequently, the existing diploid Hi-C analyses are based on sparse and inaccurate allele-specific contact matrices, which might lead to inaccurate modeling of diploid genome architecture.Here, we present ASHIC, a hierarchical Bayesian framework to model allele-specific chromatin organizations in diploid genomes. We developed two models under this Bayesian framework: the Poisson-multinomial (ASHIC-PM) model and the zero-inflated Poisson-multinomial (ASHIC-ZIPM) model. The proposed ASHIC methods impute allele-specific contact maps from diploid Hi-C data and simultaneously infer allelic 3D structures. Through simulation studies, we demonstrated that our methods outperformed existing approaches, especially under low coverage and low SNP density conditions. Additionally, we applied ASHIC-ZIPM to a published diploid mouse Hi-C data and studied the active/inactive X chromosomes and the H19/Igf2 imprinting region. In both cases, our method produced fine-resolution diploid chromatin maps and 3D structures, and provided insights into the allelic chromatin organizations and functions. To summarize, our work provides a statistically rigorous framework for investigating fine-scale allele-specific chromatin conformations.The ASHIC software is publicly available at https://github.com/wmalab/ASHIC.