scholarly journals IDR2D identifies reproducible genomic interactions

2020 ◽  
Vol 48 (6) ◽  
pp. e31-e31
Author(s):  
Konstantin Krismer ◽  
Yuchun Guo ◽  
David K Gifford
Keyword(s):  
Gene Regulation ◽  
Genome Organization ◽  
Genome Biology ◽  
Chromatin Interaction ◽  
Interaction Data ◽  
Bioconductor Package ◽  
Online Service

Abstract Chromatin interaction data from protocols such as ChIA-PET, HiChIP and Hi-C provide valuable insights into genome organization and gene regulation, but can include spurious interactions that do not reflect underlying genome biology. We introduce an extension of the Irreproducible Discovery Rate (IDR) method called IDR2D that identifies replicable interactions shared by chromatin interaction experiments. IDR2D provides a principled set of interactions and eliminates artifacts from single experiments. The method is available as a Bioconductor package for the R community, as well as an online service at https://idr2d.mit.edu.

scholarly journals IDR2D identifies reproducible genomic interactions

2019 ◽  
Author(s):  
Konstantin Krismer ◽  
Yuchun Guo ◽  
David K. Gifford
Keyword(s):  
Gene Regulation ◽  
Genome Organization ◽  
Genome Biology ◽  
Chromatin Interaction ◽  
Interaction Data ◽  
Bioconductor Package ◽  
Online Service ◽  
Link Type

AbstractChromatin interaction data from protocols such as ChIA-PET, HiChIP, and HiC provide valuable insights into genome organization and gene regulation, but can include spurious interactions that do not reflect underlying genome biology. We introduce a generalization of the Irreproducible Discovery Rate (IDR) method called IDR2D that identifies replicable interactions shared by chromatin interaction experiments. IDR2D provides a principled set of interactions and eliminates artifacts from single experiments. The method is available as a Bioconductor package for the R community, as well as an online service at https://idr2d.mit.edu.

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

2017 ◽  
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.

scholarly journals Probing multi-way chromatin interaction with hypergraph representation learning

2020 ◽  
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.

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

BMC Genomics ◽  
2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Nathan Harmston ◽  
Elizabeth Ing-Simmons ◽  
Malcolm Perry ◽  
Anja Barešić ◽  
Boris Lenhard
Keyword(s):  
Chromatin Interaction ◽  
Interaction Data ◽  
Bioconductor Package

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

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Haitham Ashoor ◽  
Xiaowen Chen ◽  
Wojciech Rosikiewicz ◽  
Jiahui Wang ◽  
Albert Cheng ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Graph Embedding ◽  
Chromatin Interaction ◽  
Interaction Data

scholarly journals Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data

2013 ◽  
Vol 14 (6) ◽  
pp. 390-403 ◽  
Author(s):  
Job Dekker ◽  
Marc A. Marti-Renom ◽  
Leonid A. Mirny
Keyword(s):  
Three Dimensional ◽  
Chromatin Interaction ◽  
Interaction Data

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

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.

scholarly journals Genomic variant annotation workflow for clinical applications

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 1963 ◽  
Author(s):  
Thomas Thurnherr ◽  
Franziska Singer ◽  
Daniel J. Stekhoven ◽  
Niko Beerenwinkel
Keyword(s):  
Gene Interaction ◽  
Clinical Applications ◽  
Interaction Data ◽  
Bioconductor Package ◽  
Variant Annotation ◽  
Interaction Database ◽  
Genomic Aberrations ◽  
Genomic Variant ◽  
Different Levels ◽  
Generation Sequencing

Annotation and interpretation of DNA aberrations identified through next-generation sequencing is becoming an increasingly important task. Even more so in the context of data analysis pipelines for medical applications, where genomic aberrations are associated with phenotypic and clinical features. Here we describe a workflow to identify potential gene targets in aberrated genes or pathways and their corresponding drugs. To this end, we provide the R/Bioconductor package rDGIdb, an R wrapper to query the drug-gene interaction database (DGIdb). DGIdb accumulates drug-gene interaction data from 15 different resources and allows filtering on different levels. The rDGIdb package makes these resources and tools available to R users. Moreover, rDGIdb queries can be automated through incorporation of the rDGIdb package into NGS sequencing pipelines.

scholarly journals Topologically associated domains enriched for lineage-specific genes reveal expression-dependent nuclear topologies during myogenesis

2016 ◽  
Vol 113 (12) ◽  
pp. E1691-E1700 ◽  
Author(s):  
Daniel S. Neems ◽  
Arturo G. Garza-Gongora ◽  
Erica D. Smith ◽  
Steven T. Kosak
Keyword(s):  
Gene Regulation ◽  
Genome Organization ◽  
Spatial Organization ◽  
Potential Role ◽  
Spatial Distance ◽  
Linear Distribution ◽  
Myotube Formation ◽  
Topologically Associated Domains ◽  
The Relationship ◽  
Lineage Specific Genes

The linear distribution of genes across chromosomes and the spatial localization of genes within the nucleus are related to their transcriptional regulation. The mechanistic consequences of linear gene order, and how it may relate to the functional output of genome organization, remain to be fully resolved, however. Here we tested the relationship between linear and 3D organization of gene regulation during myogenesis. Our analysis has identified a subset of topologically associated domains (TADs) that are significantly enriched for muscle-specific genes. These lineage-enriched TADs demonstrate an expression-dependent pattern of nuclear organization that influences the positioning of adjacent nonenriched TADs. Therefore, lineage-enriched TADs inform cell-specific genome organization during myogenesis. The reduction of allelic spatial distance of one of these domains, which contains Myogenin, correlates with reduced transcriptional variability, identifying a potential role for lineage-specific nuclear topology. Using a fusion-based strategy to decouple mitosis and myotube formation, we demonstrate that the cell-specific topology of syncytial nuclei is dependent on cell division. We propose that the effects of linear and spatial organization of gene loci on gene regulation are linked through TAD architecture, and that mitosis is critical for establishing nuclear topologies during cellular differentiation.

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