Chromatin interaction data visualization in the WashU Epigenome Browser

AbstractMotivationLong-range chromatin interactions are critical for gene regulations and genome maintenance. HiC and Cool are the two most common data formats used by the community, including the 4D Nucleome Consortium (4DN), to represent chromatin interaction data from a variety of chromatin conformation capture experiments, and specialized tools were developed for their analysis, visualization, and conversion. However, there does not exist a tool that can support visualization of both data formats simultaneously.ResultsThe WashU Epigenome Browser has integrated both HiC and Cool data formats into its visualization platform. Investigators can seamlessly explore chromatin interaction data regardless of their underlying data format. For developers it is straightforward to benchmark the differences in rendering speed and computational resource usage between the two data formats.Availabilityhttp://epigenomegateway.wustl.edu/browser/.

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Hi-Cpipe: a pipeline for high-throughput chromosome capture

10.1101/020636 ◽

2015 ◽

Cited By ~ 9

Author(s):

Giancarlo Castellano ◽

François Le Dily ◽

Antonio Hermoso Pulido ◽

Miguel Beato ◽

Guglielmo Roma

Keyword(s):

High Throughput ◽

Visual Inspection ◽

Automated Analysis ◽

Chromatin Conformation ◽

Interaction Data ◽

Alignment Quality ◽

Bioinformatics Pipeline ◽

Chromatin Interactions ◽

Genome Wide ◽

Analysis Workflow

Hi-Cpipe is a bioinformatics pipeline for the automated analysis of data generated by high-throughput chromatin conformation capture (HiC). The analysis workflow comprises steps of data formatting, genome alignment, quality control and filtering, identification of genome-wide chromatin interactions, visualization and statistics. An interactive browser enables visual inspection of interaction data and results.

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DNA Rchitect: an R based visualizer for network analysis of chromatin interaction data

Bioinformatics ◽

10.1093/bioinformatics/btz608 ◽

2019 ◽

Author(s):

R N Ramirez ◽

K Bedirian ◽

S M Gray ◽

A Diallo

Keyword(s):

Open Source ◽

Web Application ◽

Genomic Data ◽

Chromatin Interaction ◽

Chromatin Conformation ◽

Interaction Data ◽

Data Types ◽

Network Analyses ◽

Interactive Network ◽

Network Functionality

Abstract Motivation Visualization of multiple genomic data generally requires the use of public or commercially hosted browsers. Flexible visualization of chromatin interaction data as genomic features and network components offer informative insights to gene expression. An open source application for visualizing HiC and chromatin conformation-based data as 2D-arcs accompanied by interactive network analyses is valuable. Results DNA Rchitect is a new tool created to visualize HiC and chromatin conformation-based contacts at high (Kb) and low (Mb) genomic resolutions. The user can upload their pre-filtered HiC experiment in bedpe format to the DNA Rchitect web app that we have hosted or to a version they themselves have deployed. Using DNA Rchitect, the uploaded data allows the user to visualize different interactions of their sample, perform simple network analyses, while also offering visualization of other genomic data types. The user can then download their results for additional network functionality offered in network based programs such as Cytoscape. Availability and implementation DNA Rchitect is freely available both as a web application written primarily in R available at http://shiny.immgen.org/DNARchitect/ and as an open source released under an MIT license at: https://github.com/alosdiallo/DNA_Rchitect.

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The 3D Genome Browser: a web-based browser for visualizing 3D genome organization and long-range chromatin interactions

10.1101/112268 ◽

2017 ◽

Cited By ~ 34

Author(s):

Yanli Wang ◽

Bo Zhang ◽

Lijun Zhang ◽

Lin An ◽

Jie Xu ◽

...

Keyword(s):

Genome Organization ◽

Target Genes ◽

Genome Structure ◽

Regulatory Elements ◽

Genomic Region ◽

Genome Browser ◽

Chromatin Interaction ◽

Interaction Data ◽

3D Genome ◽

Chromatin Interactions

ABSTRACTRecent advent of 3C-based technologies such as Hi-C and ChIA-PET provides us an opportunity to explore chromatin interactions and 3D genome organization in an unprecedented scale and resolution. However, it remains a challenge to visualize chromatin interaction data due to its size and complexity. Here, we introduce the 3D Genome Browser (http://3dgenome.org), which allows users to conveniently explore both publicly available and their own chromatin interaction data. Users can also seamlessly integrate other “omics” data sets, such as ChIP-Seq and RNA-Seq for the same genomic region, to gain a complete view of both regulatory landscape and 3D genome structure for any given gene. Finally, our browser provides multiple methods to link distal cis-regulatory elements with their potential target genes, including virtual 4C, ChIA-PET, Capture Hi-C and cross-cell-type correlation of proximal and distal DNA hypersensitive sites, and therefore represents a valuable resource for the study of gene regulation in mammalian genomes.

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Probing multi-way chromatin interaction with hypergraph representation learning

10.1101/2020.01.22.916171 ◽

2020 ◽

Cited By ~ 1

Author(s):

Ruochi Zhang ◽

Jian Ma

Keyword(s):

Genome Organization ◽

Representation Learning ◽

Higher Order ◽

Computational Method ◽

Chromatin Interaction ◽

Interaction Data ◽

3D Genome ◽

Chromatin Interactions ◽

Single Nucleus ◽

And Function

AbstractAdvances in high-throughput mapping of 3D genome organization have enabled genome-wide characterization of chromatin interactions. However, proximity ligation based mapping approaches for pairwise chromatin interaction such as Hi-C cannot capture multi-way interactions, which are informative to delineate higher-order genome organization and gene regulation mechanisms at single-nucleus resolution. The very recent development of ligation-free chromatin interaction mapping methods such as SPRITE and ChIA-Drop has offered new opportunities to uncover simultaneous interactions involving multiple genomic loci within the same nuclei. Unfortunately, methods for analyzing multi-way chromatin interaction data are significantly underexplored. Here we develop a new computational method, called MATCHA, based on hypergraph representation learning where multi-way chromatin interactions are represented as hyperedges. Applications to SPRITE and ChIA-Drop data suggest that MATCHA is effective to denoise the data and make de novo predictions of multi-way chromatin interactions, reducing the potential false positives and false negatives from the original data. We also show that MATCHA is able to distinguish between multi-way interaction in a single nucleus and combination of pairwise interactions in a cell population. In addition, the embeddings from MATCHA reflect 3D genome spatial localization and function. MATCHA provides a promising framework to significantly improve the analysis of multi-way chromatin interaction data and has the potential to offer unique insights into higher-order chromosome organization and function.

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Three-dimensional chromatin interactions remain stable upon CAG/CTG repeat expansion

Science Advances ◽

10.1126/sciadv.aaz4012 ◽

2020 ◽

Vol 6 (27) ◽

pp. eaaz4012 ◽

Cited By ~ 2

Author(s):

Gustavo A. Ruiz Buendía ◽

Marion Leleu ◽

Flavia Marzetta ◽

Ludovica Vanzan ◽

Jennifer Y. Tan ◽

...

Keyword(s):

Gene Expression ◽

Three Dimensional ◽

Cag Repeat ◽

Ctcf Binding ◽

Chromatin Interaction ◽

Chromatin Conformation ◽

Repeat Instability ◽

Ctg Repeats ◽

Chromatin Interactions ◽

Ctg Repeat

Expanded CAG/CTG repeats underlie 13 neurological disorders, including myotonic dystrophy type 1 (DM1) and Huntington’s disease (HD). Upon expansion, disease loci acquire heterochromatic characteristics, which may provoke changes to chromatin conformation and thereby affect both gene expression and repeat instability. Here, we tested this hypothesis by performing 4C sequencing at the DMPK and HTT loci from DM1 and HD–derived cells. We find that allele sizes ranging from 15 to 1700 repeats displayed similar chromatin interaction profiles. This was true for both loci and for alleles with different DNA methylation levels and CTCF binding. Moreover, the ectopic insertion of an expanded CAG repeat tract did not change the conformation of the surrounding chromatin. We conclude that CAG/CTG repeat expansions are not enough to alter chromatin conformation in cis. Therefore, it is unlikely that changes in chromatin interactions drive repeat instability or changes in gene expression in these disorders.

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Functional annotation of human long noncoding RNAs using chromatin conformation data

10.1101/2021.01.13.426305 ◽

2021 ◽

Author(s):

Saumya Agrawal ◽

Tanvir Alam ◽

Masaru Koido ◽

Ivan V. Kulakovskiy ◽

Jessica Severin ◽

...

Keyword(s):

Functional Annotation ◽

Rna Binding ◽

Functional Characterization ◽

Cell Types ◽

Chromatin Interaction ◽

Spatial Proximity ◽

Chromatin Conformation ◽

Cell Type ◽

Cell Type Specific ◽

Rna Domains

AbstractTranscription of the human genome yields mostly long non-coding RNAs (lncRNAs). Systematic functional annotation of lncRNAs is challenging due to their low expression level, cell type-specific occurrence, poor sequence conservation between orthologs, and lack of information about RNA domains. Currently, 95% of human lncRNAs have no functional characterization. Using chromatin conformation and Cap Analysis of Gene Expression (CAGE) data in 18 human cell types, we systematically located genomic regions in spatial proximity to lncRNA genes and identified functional clusters of interacting protein-coding genes, lncRNAs and enhancers. Using these clusters we provide a cell type-specific functional annotation for 7,651 out of 14,198 (53.88%) lncRNAs. LncRNAs tend to have specialized roles in the cell type in which it is first expressed, and to incorporate more general functions as its expression is acquired by multiple cell types during evolution. By analyzing RNA-binding protein and RNA-chromatin interaction data in the context of the spatial genomic interaction map, we explored mechanisms by which these lncRNAs can act.

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multiHiCcompare: joint normalization and comparative analysis of complex Hi-C experiments

Bioinformatics ◽

10.1093/bioinformatics/btz048 ◽

2019 ◽

Vol 35 (17) ◽

pp. 2916-2923 ◽

Cited By ~ 15

Author(s):

John C Stansfield ◽

Kellen G Cresswell ◽

Mikhail G Dozmorov

Keyword(s):

Comparative Analysis ◽

A Priori ◽

Three Dimensional ◽

R Package ◽

Supplementary Information ◽

Chromatin Interaction ◽

Model Framework ◽

Chromatin Interactions ◽

Loess Regression ◽

Sequencing Studies

Abstract Motivation With the development of chromatin conformation capture technology and its high-throughput derivative Hi-C sequencing, studies of the three-dimensional interactome of the genome that involve multiple Hi-C datasets are becoming available. To account for the technology-driven biases unique to each dataset, there is a distinct need for methods to jointly normalize multiple Hi-C datasets. Previous attempts at removing biases from Hi-C data have made use of techniques which normalize individual Hi-C datasets, or, at best, jointly normalize two datasets. Results Here, we present multiHiCcompare, a cyclic loess regression-based joint normalization technique for removing biases across multiple Hi-C datasets. In contrast to other normalization techniques, it properly handles the Hi-C-specific decay of chromatin interaction frequencies with the increasing distance between interacting regions. multiHiCcompare uses the general linear model framework for comparative analysis of multiple Hi-C datasets, adapted for the Hi-C-specific decay of chromatin interaction frequencies. multiHiCcompare outperforms other methods when detecting a priori known chromatin interaction differences from jointly normalized datasets. Applied to the analysis of auxin-treated versus untreated experiments, and CTCF depletion experiments, multiHiCcompare was able to recover the expected epigenetic and gene expression signatures of loss of chromatin interactions and reveal novel insights. Availability and implementation multiHiCcompare is freely available on GitHub and as a Bioconductor R package https://bioconductor.org/packages/multiHiCcompare. Supplementary information Supplementary data are available at Bioinformatics online.

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Whole Earth Telescope Headquarters Standard Data Formats

Open Astronomy ◽

10.1515/astro-2017-0043 ◽

2003 ◽

Vol 12 (2) ◽

Author(s):

R. L. Riddle ◽

S. D. Kawaler

Keyword(s):

Next Generation ◽

Ccd Photometry ◽

Data Format ◽

Final Version ◽

Standard Data ◽

Data Formats ◽

Whole Earth

AbstractAs the WET moves to CCD systems, we move away from the uniformity of the standard WET photometer into an arena where each system can be radically different. There are many possible CCD photometry systems that can fulfil the requirements of a WET instrument, but each of these will have their own unique native data format. During XCov22, it became readily apparent that the WET requires a defined data format for all CCD data that arrives at HQ. This paper describes the proposed format for the next generation of WET data; the final version will be the default format for XQED, the new photometry package discussed elsewhere in these proceedings.

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