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1. HiCARN: resolution enhancement of Hi-C data using cascading residual networks

   https://doi.org/10.1093/bioinformatics/btac156  

   Hicks, Parker ; Oluwadare, Oluwatosin ; Robinson, ed., Peter ( March 2022 , Bioinformatics)

   High throughput chromosome conformation capture (Hi-C) contact matrices are used to predict 3D chromatin structures in eukaryotic cells. High-resolution Hi-C data are less available than low-resolution Hi-C data due to sequencing costs but provide greater insight into the intricate details of 3D chromatin structures such as enhancer–promoter interactions and sub-domains. To provide a cost-effective solution to high-resolution Hi-C data collection, deep learning models are used to predict high-resolution Hi-C matrices from existing low-resolution matrices across multiple cell types.

   Here, we present two Cascading Residual Networks called HiCARN-1 and HiCARN-2, a convolutional neural network and a generative adversarial network, that use a novel framework of cascading connections throughout the network for Hi-C contact matrix prediction from low-resolution data. Shown by image evaluation and Hi-C reproducibility metrics, both HiCARN models, overall, outperform state-of-the-art Hi-C resolution enhancement algorithms in predictive accuracy for both human and mouse 1/16, 1/32, 1/64 and 1/100 downsampled high-resolution Hi-C data. Also, validation by extracting topologically associating domains, chromosome 3D structure and chromatin loop predictions from the enhanced data shows that HiCARN can proficiently reconstruct biologically significant regions.

   HiCARN can be accessed and utilized as an open-sourced software at: https://github.com/OluwadareLab/HiCARN and is also available as a containerized application that can be run on any platform.

   Supplementary data are available at Bioinformatics online.

    

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2. Quantifying the similarity of topological domains across normal and cancer human cell types

   https://doi.org/10.1093/bioinformatics/bty265  

   Sauerwald, Natalie ; Kingsford, Carl ( June 2018 , Bioinformatics)

   Three-dimensional chromosome structure has been increasingly shown to influence various levels of cellular and genomic functions. Through Hi-C data, which maps contact frequency on chromosomes, it has been found that structural elements termed topologically associating domains (TADs) are involved in many regulatory mechanisms. However, we have little understanding of the level of similarity or variability of chromosome structure across cell types and disease states. In this study, we present a method to quantify resemblance and identify structurally similar regions between any two sets of TADs.

   We present an analysis of 23 human Hi-C samples representing various tissue types in normal and cancer cell lines. We quantify global and chromosome-level structural similarity, and compare the relative similarity between cancer and non-cancer cells. We find that cancer cells show higher structural variability around commonly mutated pan-cancer genes than normal cells at these same locations.

   Software for the methods and analysis can be found at https://github.com/Kingsford-Group/localtadsim

    

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3. A Heuristic Strategy for Multi-Mapping Reads to Enhance Hi-C Data

   https://doi.org/10.1109/BIBE52308.2021.9635215  

   Bulathsinghalage, Chanaka ; Liu, Lu ( October 2021 , 2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE))

   Current Hi-C analysis approaches focus on uniquely mapped reads and little research has been carried out to include multi-mapping reads, which leads to a lack of biological signals from DNA repetitive regions. We propose a heuristic strategy to assign multi-mapping reads to loci according to the distance to their closest restriction enzyme cutting sites. We demonstrate that the heuristic strategy can rescue multi-mapping reads thus enhance the quality of Hi-C data. Compared with mHi-C, it not only improves replicate reproducibility in the same cell type, but also maintains the difference between replicates of different cell types. Moreover, the strategy identifies much more common statistically significant chromatin interactions between Hi-C experiments of different restriction enzymes and has a huge advantage on computing resources. Therefore, the heuristic strategy can be used to enhance Hi-C data by utilizing multi-mapping reads. 

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4. scHiCDiff: detecting differential chromatin interactions in single-cell Hi-C data

   https://doi.org/10.1093/bioinformatics/btad625  

   Liu, Huiling ; Ma, Wenxiu ; Marschall, ed., Tobias ( October 2023 , Bioinformatics)

   Here, we presented the scHiCDiff software tool that provides both nonparametric tests and parametirc models to detect differential chromatin interactions (DCIs) from single-cell Hi-C data. We thoroughly evaluated the scHiCDiff methods on both simulated and real data. Our results demonstrated that scHiCDiff, especially the zero-inflated negative binomial model option, can effectively detect reliable and consistent single-cell DCIs between two conditions, thereby facilitating the study of cell type-specific variations of chromatin structures at the single-cell level.

   scHiCDiff is implemented in R and freely available at GitHub (https://github.com/wmalab/scHiCDiff).

    

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5. ASHIC: hierarchical Bayesian modeling of diploid chromatin contacts and structures

   https://doi.org/10.1093/nar/gkaa872  

   Ye, Tiantian ; Ma, Wenxiu ( October 2020 , Nucleic Acids Research)

   null (Ed.)

   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 incorrect 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 the 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 ASHIC methods outperformed existing approaches, especially under low coverage and low SNP density conditions. Additionally, in the analyses of diploid Hi-C datasets in mouse and human, our ASHIC-ZIPM 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. 

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