Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data

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.

Download Full-text

Revealing Long-Range Interconnected Hubs in Human Chromatin Interaction Data Using Graph Theory

Physical Review Letters ◽

10.1103/physrevlett.111.118102 ◽

2013 ◽

Vol 111 (11) ◽

Cited By ~ 31

Author(s):

R. E. Boulos ◽

A. Arneodo ◽

P. Jensen ◽

B. Audit

Keyword(s):

Graph Theory ◽

Long Range ◽

Chromatin Interaction ◽

Interaction Data

Download Full-text

Multiplex chromatin interaction analysis by signal processing and statistical algorithms

10.1101/665232 ◽

2019 ◽

Cited By ~ 1

Author(s):

Minji Kim ◽

Meizhen Zheng ◽

Simon Zhongyuan Tian ◽

Daniel Capurso ◽

Byoungkoo Lee ◽

...

Keyword(s):

Single Molecule ◽

Chromatin Immunoprecipitation ◽

Genome Mapping ◽

Interaction Analysis ◽

Statistical Significance ◽

Chromatin Interaction ◽

Interaction Data ◽

Topological Domains ◽

3D Genome ◽

Algorithmic Framework

AbstractThe single-molecule multiplex chromatin interaction data generated by emerging non-ligation-based 3D genome mapping technologies provide novel insights into high dimensional chromatin organization, yet introduce new computational challenges. We developed MIA-Sig (https://github.com/TheJacksonLaboratory/mia-sig.git), an algorithmic framework to de-noise the data, assess the statistical significance of chromatin complexes, and identify topological domains and inter-domain contacts. On chromatin immunoprecipitation (ChIP)-enriched data, MIA-Sig can clearly distinguish the protein-associated interactions from the non-specific topological domains.

Download Full-text

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.

Download Full-text

Exploring chromatin conformation and gene co-expression through graph embedding

Bioinformatics ◽

10.1093/bioinformatics/btaa803 ◽

2020 ◽

Vol 36 (Supplement_2) ◽

pp. i700-i708

Author(s):

Marco Varrone ◽

Luca Nanni ◽

Giovanni Ciriello ◽

Stefano Ceri

Keyword(s):

Interaction Network ◽

Graph Embedding ◽

Relevant Information ◽

Supplementary Information ◽

Chromatin Interaction ◽

Physical Interaction ◽

Chromatin Conformation ◽

Interaction Patterns ◽

Chromosome Conformation ◽

Topological Features

Abstract Motivation The relationship between gene co-expression and chromatin conformation is of great biological interest. Thanks to high-throughput chromosome conformation capture technologies (Hi-C), researchers are gaining insights on the tri-dimensional organization of the genome. Given the high complexity of Hi-C data and the difficult definition of gene co-expression networks, the development of proper computational tools to investigate such relationship is rapidly gaining the interest of researchers. One of the most fascinating questions in this context is how chromatin topology correlates with gene co-expression and which physical interaction patterns are most predictive of co-expression relationships. Results To address these questions, we developed a computational framework for the prediction of co-expression networks from chromatin conformation data. We first define a gene chromatin interaction network where each gene is associated to its physical interaction profile; then, we apply two graph embedding techniques to extract a low-dimensional vector representation of each gene from the interaction network; finally, we train a classifier on gene embedding pairs to predict if they are co-expressed. Both graph embedding techniques outperform previous methods based on manually designed topological features, highlighting the need for more advanced strategies to encode chromatin information. We also establish that the most recent technique, based on random walks, is superior. Overall, our results demonstrate that chromatin conformation and gene regulation share a non-linear relationship and that gene topological embeddings encode relevant information, which could be used also for downstream analysis. Availability and implementation The source code for the analysis is available at: https://github.com/marcovarrone/gene-expression-chromatin. Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

Chromatin interaction data visualization in the WashU Epigenome Browser

10.1101/239368 ◽

2017 ◽

Author(s):

Daofeng Li ◽

Silas Hsu ◽

Deepak Purushotham ◽

Ting Wang

Keyword(s):

Data Visualization ◽

Chromatin Interaction ◽

Chromatin Conformation ◽

Interaction Data ◽

Computational Resource ◽

Genome Maintenance ◽

Data Format ◽

Chromatin Interactions ◽

Data Formats ◽

Gene Regulations

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/.

Download Full-text

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.

Download Full-text

GenomicInteractions: An R/Bioconductor package for manipulating and investigating chromatin interaction data

BMC Genomics ◽

10.1186/s12864-015-2140-x ◽

2015 ◽

Vol 16 (1) ◽

Cited By ~ 26

Author(s):

Nathan Harmston ◽

Elizabeth Ing-Simmons ◽

Malcolm Perry ◽

Anja Barešić ◽

Boris Lenhard

Keyword(s):

Chromatin Interaction ◽

Interaction Data ◽

Bioconductor Package

Download Full-text