HIFI: estimating DNA-DNA interaction frequency from Hi-C data at restriction-fragment resolution

Cameron, Christopher J.F.; Dostie, Josée; Blanchette, Mathieu

doi:10.1186/s13059-019-1913-y

Cited by 29 publications

(30 citation statements)

References 69 publications

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“…Data resolution restricts the exploration of finer 3D chromatin organization [ 109 ]. Many computational algorithms, such as DeepHiC [ 110 ], HIFI [ 111 ], Boost-HiC [ 112 ], deDoc [ 113 ], hicGAN [ 114 ], HiCNN [ 115 ] and HiCPlus [ 11 ], have been developed to enhance the resolution of Hi-C data. Second, limitations remain regarding the strategies applied in state-of-art computational methods.…”

Section: Discussionmentioning

confidence: 99%

Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles

Huan

et al. 2021

Briefings in Bioinformatics

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The exploration of three-dimensional chromatin interaction and organization provides insight into mechanisms underlying gene regulation, cell differentiation and disease development. Advances in chromosome conformation capture technologies, such as high-throughput chromosome conformation capture (Hi-C) and chromatin interaction analysis by paired-end tag (ChIA-PET), have enabled the exploration of chromatin interaction and organization. However, high-resolution Hi-C and ChIA-PET data are only available for a limited number of cell lines, and their acquisition is costly, time consuming, laborious and affected by theoretical limitations. Increasing evidence shows that DNA sequence and epigenomic features are informative predictors of regulatory interaction and chromatin architecture. Based on these features, numerous computational methods have been developed for the prediction of chromatin interaction and organization, whereas they are not extensively applied in biomedical study. A systematical study to summarize and evaluate such methods is still needed to facilitate their application. Here, we summarize 48 computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles, categorize them and compare their performance. Besides, we provide a comprehensive guideline for the selection of suitable methods to predict chromatin interaction and organization based on available data and biological question of interest.

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Section: Discussionmentioning

confidence: 99%

Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles

Huan

et al. 2021

Briefings in Bioinformatics

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“…For example, side information pertaining to the genomic distance of the current submatrix to be predicted may provide beneficial results, improving the model's optimization behaviour. In addition to improving upon the methodology of HiCSR, work exploring the relative trade-offs between deep learning Hi-C enhancement methods (such as those discussed here) and recently proposed alternative methods such as HIFI (Cameron et al, 2020) is required to better understand the scenarios in which each technique is preferred.…”

Section: Discussionmentioning

confidence: 99%

HiCSR: a Hi-C super-resolution framework for producing highly realistic contact maps

Dimmick

2020

Preprint

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AbstractMotivationHi-C data has enabled the genome-wide study of chromatin folding and architecture, and has led to important discoveries in the structure and function of chromatin conformation. Here, high resolution data plays a particularly important role as many chromatin substructures such as Topologically Associating Domains (TADs) and chromatin loops cannot be adequately studied with low resolution contact maps. However, the high sequencing costs associated with the generation of high resolution Hi-C data has become an experimental barrier. Data driven machine learning models, which allow low resolution Hi-C data to be computationally enhanced, offer a promising avenue to address this challenge.ResultsBy carefully examining the properties of Hi-C maps and integrating various recent advances in deep learning, we developed a Hi-C Super-Resolution (HiCSR) framework capable of accurately recovering the fine details, textures, and substructures found in high resolution contact maps. This was achieved using a novel loss function tailored to the Hi-C enhancement problem which optimizes for an adversarial loss from a Generative Adversarial Network (GAN), a feature reconstruction loss derived from the latent representation of a denoising autoencoder, and a pixel-wise loss. Not only can the resulting framework generate enhanced Hi-C maps more visually similar to the original high resolution maps, it also excels on a suite of reproducibility metrics produced by members of the ENCODE Consortium compared to existing approaches, including HiCPlus, HiCNN, hicGAN and DeepHiC. Finally, we demonstrate that HiCSR is capable of enhancing Hi-C data across sequencing depth, cell types, and species, recovering biologically significant contact domain boundaries.AvailabilityWe make our implementation available for download at: https://github.com/PSI-Lab/HiCSRContactljlee@psi.toronto.eduSupplementary informationAvailable Online

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“…Only few studies have focused on tissues from organs [ 211 , 212 ], and most of them have a small number of read pairs, which cannot identify all chromatin loops but only identifies large TADs (Table 4 ). Higher genome coverage is recommended to perform comparison analyses between Hi-C data sets and call chromatin loops for regulatory elements [ 177 , 213 ]. Therefore, additional higher resolution data sets using Hi-C or 3C-derived methods are greatly needed.…”

Section: Main Textmentioning

confidence: 99%

Molecular and computational approaches to map regulatory elements in 3D chromatin structure

Lee

Rhie

2021

Epigenetics & Chromatin

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Epigenetic marks do not change the sequence of DNA but affect gene expression in a cell-type specific manner by altering the activities of regulatory elements. Development of new molecular biology assays, sequencing technologies, and computational approaches enables us to profile the human epigenome in three-dimensional structure genome-wide. Here we describe various molecular biology techniques and bioinformatic tools that have been developed to measure the activities of regulatory elements and their chromatin interactions. Moreover, we list currently available three-dimensional epigenomic data sets that are generated in various human cell types and tissues to assist in the design and analysis of research projects.

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HIFI: estimating DNA-DNA interaction frequency from Hi-C data at restriction-fragment resolution

Cited by 29 publications

References 69 publications

Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles

Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles

HiCSR: a Hi-C super-resolution framework for producing highly realistic contact maps

Molecular and computational approaches to map regulatory elements in 3D chromatin structure

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