Genome-wide Hi-C Analyses in Wild-Type and Mutants Reveal High-Resolution Chromatin Interactions in Arabidopsis

Abstract Motivation Chromatin interactions play an important role in genome architecture and gene regulation. The Hi-C assay generates such interactions maps genome-wide, but at relatively low resolutions (e.g. 5-25 kb), which is substantially coarser than the resolution of transcription factor binding sites or open chromatin sites that are potential sources of such interactions. Results To predict the sources of Hi-C-identified interactions at a high resolution (e.g. 100 bp), we developed a computational method that integrates data from DNase-seq and ChIP-seq of TFs and histone marks. Our method, χ-CNN, uses this data to first train a convolutional neural network (CNN) to discriminate between called Hi-C interactions and non-interactions. χ-CNN then predicts the high-resolution source of each Hi-C interaction using a feature attribution method. We show these predictions recover original Hi-C peaks after extending them to be coarser. We also show χ-CNN predictions enrich for evolutionarily conserved bases, eQTLs and CTCF motifs, supporting their biological significance. χ-CNN provides an approach for analyzing important aspects of genome architecture and gene regulation at a higher resolution than previously possible. Availability and implementation χ-CNN software is available on GitHub (https://github.com/ernstlab/X-CNN). Supplementary information Supplementary data are available at Bioinformatics online.

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HiCNN2: Enhancing the Resolution of Hi-C Data Using an Ensemble of Convolutional Neural Networks

Genes ◽

10.3390/genes10110862 ◽

2019 ◽

Vol 10 (11) ◽

pp. 862

Author(s):

Tong Liu ◽

Zheng Wang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

High Resolution ◽

Convolutional Neural Network ◽

Mean Squared Error ◽

Correlation Coefficients ◽

Chromatin Interactions ◽

Squared Error ◽

Genome Wide

We present a deep-learning package named HiCNN2 to learn the mapping between low-resolution and high-resolution Hi-C (a technique for capturing genome-wide chromatin interactions) data, which can enhance the resolution of Hi-C interaction matrices. The HiCNN2 package includes three methods each with a different deep learning architecture: HiCNN2-1 is based on one single convolutional neural network (ConvNet); HiCNN2-2 consists of an ensemble of two different ConvNets; and HiCNN2-3 is an ensemble of three different ConvNets. Our evaluation results indicate that HiCNN2-enhanced high-resolution Hi-C data achieve smaller mean squared error and higher Pearson’s correlation coefficients with experimental high-resolution Hi-C data compared with existing methods HiCPlus and HiCNN. Moreover, all of the three HiCNN2 methods can recover more significant interactions detected by Fit-Hi-C compared to HiCPlus and HiCNN. Based on our evaluation results, we would recommend using HiCNN2-1 and HiCNN2-3 if recovering more significant interactions from Hi-C data is of interest, and HiCNN2-2 and HiCNN if the goal is to achieve higher reproducibility scores between the enhanced Hi-C matrix and the real high-resolution Hi-C matrix.

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HiCORE: Hi-C analysis for identification of core chromatin loops with higher resolution and reliability

10.1101/2020.10.01.322024 ◽

2020 ◽

Author(s):

Hongwoo Lee ◽

Pil Joon Seo

Keyword(s):

High Resolution ◽

High Throughput Sequencing ◽

Stochastic Noise ◽

3D Interaction ◽

Chromosome Conformation ◽

Genome Coverage ◽

Chromatin Loops ◽

Chromatin Interactions ◽

Genome Wide ◽

Physical Interactions

AbstractGenome-wide chromosome conformation capture (3C)-based high-throughput sequencing (Hi-C) has enabled identification of genome-wide chromatin loops. Because the Hi-C map with restriction fragment resolution is intrinsically associated with sparsity and stochastic noise, Hi-C data are usually binned at particular intervals; however, the binning method has limited reliability, especially at high resolution. Here, we describe a new method called HiCORE, which provides simple pipelines and algorithms to overcome the limitations of single-layered binning and predict core chromatin regions with 3D physical interactions. In this approach, multiple layers of binning with slightly shifted genome coverage are generated, and interacting bins at each layer are integrated to infer narrower regions of chromatin interactions. HiCORE predicts chromatin looping regions with higher resolution and contributes to the identification of the precise positions of potential genomic elements.Author SummaryThe Hi-C analysis has enabled to obtain information on 3D interaction of genomes. While various approaches have been developed for the identification of reliable chromatin loops, binning methods have been limitedly improved. We here developed HiCORE algorithm that generates multiple layers of bin-array and specifies core chromatin regions with 3D interactions. We validated our algorithm and provided advantages over conventional binning method. Overall, HiCORE facilitates to predict chromatin loops with higher resolution and reliability, which is particularly relevant in analysis of small genomes.

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An Integrative Approach for Fine-Mapping Chromatin Interactions

10.1101/605576 ◽

2019 ◽

Author(s):

Artur Jaroszewicz ◽

Jason Ernst

Keyword(s):

High Resolution ◽

Binding Sites ◽

Biological Significance ◽

Computational Method ◽

Integrative Approach ◽

Genome Architecture ◽

Open Chromatin ◽

Chromatin Interactions ◽

Genome Wide ◽

Evolutionarily Conserved

AbstractChromatin interactions play an important role in genome architecture and regulation. The Hi-C assay generates such interactions maps genome-wide, but at relatively low resolutions (e.g., 5-25kb), which is substantially larger than the resolution of transcription factor binding sites or open chromatin sites that are potential sources of such interactions. To predict the sources of Hi-C identified interactions at a high resolution (e.g., 100bp), we developed a computational method that integrates ChIP-seq data of transcription factors and histone marks and DNase-seq data. Our method,χ-SCNN, uses this data to first train a Siamese Convolutional Neural Network (SCNN) to discriminate between called Hi-C interactions and non-interactions.χ-SCNN then predicts the high-resolution source of each Hi-C interaction using a feature attribution method. We show these predictions recover original Hi-C peaks after extending them to be coarser. We also showχ-SCNN predictions enrich for evolutionarily conserved bases, eQTLs, and CTCF motifs, supporting their biological significance.χ-SCNN provides an approach for analyzing important aspects of genome architecture and regulation at a higher resolution than previously possible.χ-SCNN software is available on GitHub (https://github.com/ernstlab/X-SCNN).

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Accessible Region Conformation Capture (ARC-C) gives high resolution insights into genome architecture and regulation

Genome Research ◽

10.1101/gr.275669.121 ◽

2021 ◽

pp. gr.275669.121

Author(s):

Ni Huang ◽

Wei Qiang Seow ◽

Alex Appert ◽

Yan Dong ◽

Przemyslaw Stempor ◽

...

Keyword(s):

High Resolution ◽

Regulatory Elements ◽

Genome Architecture ◽

Regulatory Interactions ◽

C Elegans ◽

Chromatin Regulators ◽

Chromatin Interactions ◽

Genome Wide ◽

Accessible Region ◽

Domain Level

Nuclear organization and chromatin interactions are important for genome function, yet determining chromatin connections at high-resolution remains a major challenge. To address this, we developed Accessible Region Conformation Capture (ARC-C), which profiles interactions between regulatory elements genome-wide without a capture step. Applied to C. elegans, we identify ~15,000 significant interactions between regulatory elements at 500bp resolution. Of 105 TFs or chromatin regulators tested, we find that the binding sites of 60 are enriched for interacting with each other, making them candidates for mediating interactions. These include cohesin and condensin II. Applying ARC-C to a mutant of transcription factor BLMP-1 detected changes in interactions between its targets. ARC-C simultaneously profiles domain level architecture, and we observe that C. elegans chromatin domains defined by either active or repressive modifications form topologically associating domains (TADs) which interact with A/B (active/inactive) compartment-like structure. Furthermore, we discovered that inactive compartment interactions are dependent on H3K9 methylation. ARC-C is a powerful new tool to interrogate genome architecture and regulatory interactions at high resolution.

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Hi-C 2.0: An Optimized Hi-C Procedure for High-Resolution Genome-Wide Mapping of Chromosome Conformation

10.1101/090001 ◽

2016 ◽

Cited By ~ 1

Author(s):

Houda Belaghzal ◽

Job Dekker ◽

Johan H. Gibcus

Keyword(s):

High Resolution ◽

Chromosome Conformation Capture ◽

Digital Information ◽

Dna Loops ◽

Chromosome Conformation ◽

Chromatin Interactions ◽

Genome Wide

ABSTRACTChromosome conformation capture-based methods such as Hi-C have become mainstream techniques for the study of the 3D organization of genomes. These methods convert chromatin interactions reflecting topological chromatin structures into digital information (counts of pair-wise interactions). Here, we describe an updated protocol for Hi-C (Hi-C 2.0) that integrates recent improvements into a single protocol for efficient and high-resolution capture of chromatin interactions. This protocol combines chromatin digestion and frequently cutting enzymes to obtain kilobase (Kb) resolution. It also includes steps to reduce random ligation and the generation of uninformative molecules, such as unligated ends, to improve the amount of valid intra-chromosomal read pairs. This protocol allows for obtaining information on conformational structures such as compartment and TADs, as well as high-resolution conformational features such as DNA loops.

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