Detecting Spatial Chromatin Organization by Chromosome Conformation Capture II: Genome-Wide Profiling by Hi-C

2016 ◽  
pp. 47-74 ◽  
Author(s):  
Matteo Vietri Rudan ◽  
Suzana Hadjur ◽  
Tom Sexton
Keyword(s):  
Chromatin Organization ◽  
Chromosome Conformation Capture ◽  
Chromosome Conformation ◽  
Genome Wide

scholarly journals epiTAD: a web application for visualizing chromosome conformation capture data in the context of genetic epidemiology

2019 ◽  
Vol 35 (21) ◽  
pp. 4462-4464
Author(s):  
Jordan H Creed ◽  
Garrick Aden-Buie ◽  
Alvaro N Monteiro ◽  
Travis A Gerke
Keyword(s):  
Genetic Epidemiology ◽  
In Silico ◽  
Web Application ◽  
Large Scale ◽  
Source Code ◽  
Chromosome Conformation Capture ◽  
Chromosome Conformation ◽  
Web Based ◽  
Genome Wide ◽  
Public Data

Abstract Summary Complementary advances in genomic technology and public data resources have created opportunities for researchers to conduct multifaceted examination of the genome on a large scale. To meet the need for integrative genome wide exploration, we present epiTAD. This web-based tool enables researchers to compare genomic 3D organization and annotations across multiple databases in an interactive manner to facilitate in silico discovery. Availability and implementation epiTAD can be accessed at https://apps.gerkelab.com/epiTAD/ where we have additionally made publicly available the source code and a Docker containerized version of the application.

scholarly journals epiTAD: a web application for visualizing high throughput chromosome conformation capture data in the context of genetic epidemiology

2018 ◽  
Author(s):  
Jordan H. Creed ◽  
Garrick Aden-Buie ◽  
Alvaro N. Monteiro ◽  
Travis A. Gerke
Keyword(s):  
High Throughput ◽  
Genetic Epidemiology ◽  
In Silico ◽  
Web Application ◽  
Chromosome Conformation Capture ◽  
Chromosome Conformation ◽  
Web Based ◽  
Genome Wide ◽  
Public Data ◽  
Complex Scale

AbstractThe increasing availability of public data resources coupled with advancements in genomic technology has created greater opportunities for researchers to examine the genome on a large and complex scale. To meet the need for integrative genome wide exploration, we present epiTAD. This web-based tool enables researchers to compare genomic structures and annotations across multiple databases and platforms in an interactive manner in order to facilitate in silico discovery. epiTAD can be accessed at https://apps.gerkelab.com/epiTAD/.

scholarly journals No kissing in the nucleus: Unbiased analysis reveals no evidence of trans chromosomal regulation of mammalian immune development

2017 ◽  
Author(s):  
Timothy M. Johanson ◽  
Hannah D. Coughlan ◽  
Aaron T.L. Lun ◽  
Naiara G. Bediaga ◽  
Gaetano Naselli ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Fate ◽  
Immune Cells ◽  
Chromosome Conformation Capture ◽  
Chromosome Conformation ◽  
Immune Development ◽  
Regulate Gene Expression ◽  
Genome Wide ◽  
Capture Technique ◽  
Regulate Gene

SummaryIt has been proposed that interactions between mammalian chromosomes, or transchromosomal interactions (also known as kissing chromosomes), regulate gene expression and cell fate determination. Here we aimed to identify novel transchromosomal interactions in immune cells by high-resolution genome-wide chromosome conformation capture. Although we readily identified stable interactions in cis, and also between centromeres and telomeres on different chromosomes, surprisingly we identified no gene regulatory transchromosomal interactions in either mouse or human cells, including previously described interactions. We suggest that advances in the chromosome conformation capture technique and the unbiased nature of this approach allow more reliable capture of interactions between chromosomes than previous methods. Overall our findings suggest that stable transchromosomal interactions that regulate gene expression are not present in mammalian immune cells and that lineage identity is governed by cis, not trans chromosomal interactions.

A statistical model of intra-chromosome contact maps

Soft Matter ◽  
2015 ◽  
Vol 11 (5) ◽  
pp. 1019-1025 ◽  
Author(s):  
Leonid I. Nazarov ◽  
Mikhail V. Tamm ◽  
Vladik A. Avetisov ◽  
Sergei K. Nechaev
Keyword(s):  
Fine Structure ◽  
Statistical Model ◽  
Chromosome Conformation Capture ◽  
Chromosome Conformation ◽  
Contact Maps ◽  
Genome Wide ◽  
A Genome ◽  
Capture Method ◽  
Chromosome Maps

A statistical model describing a fine structure of the intra-chromosome maps obtained by a genome-wide chromosome conformation capture method (Hi–C) is proposed.

scholarly journals Bayesian inference of chromatin structure ensembles from population-averaged contact data

2020 ◽  
Vol 117 (14) ◽  
pp. 7824-7830 ◽  
Author(s):  
Simeon Carstens ◽  
Michael Nilges ◽  
Michael Habeck
Keyword(s):  
Chromatin Structure ◽  
Spatial Organization ◽  
Chromatin Organization ◽  
Chromosome Conformation Capture ◽  
Consensus Structure ◽  
Sample Population ◽  
Multistate Models ◽  
Chromosome Conformation ◽  
A Genome ◽  
Cell To Cell Variability

Mounting experimental evidence suggests a role for the spatial organization of chromatin in crucial processes of the cell nucleus such as transcription regulation. Chromosome conformation capture techniques allow us to characterize chromatin structure by mapping contacts between chromosomal loci on a genome-wide scale. The most widespread modality is to measure contact frequencies averaged over a population of cells. Single-cell variants exist, but suffer from low contact numbers and have not yet gained the same resolution as population methods. While intriguing biological insights have already been garnered from ensemble-averaged data, information about three-dimensional (3D) genome organization in the underlying individual cells remains largely obscured because the contact maps show only an average over a huge population of cells. Moreover, computational methods for structure modeling of chromatin have mostly focused on fitting a single consensus structure, thereby ignoring any cell-to-cell variability in the model itself. Here, we propose a fully Bayesian method to infer ensembles of chromatin structures and to determine the optimal number of states in a principled, objective way. We illustrate our approach on simulated data and compute multistate models of chromatin from chromosome conformation capture carbon copy (5C) data. Comparison with independent data suggests that the inferred ensembles represent the underlying sample population faithfully. Harnessing the rich information contained in multistate models, we investigate cell-to-cell variability of chromatin organization into topologically associating domains, thus highlighting the ability of our approach to deliver insights into chromatin organization of great biological relevance.

scholarly journals Targeted high-resolution chromosome conformation capture at genome-wide scale

2020 ◽  
Author(s):  
Damien J. Downes ◽  
Matthew E. Gosden ◽  
Jelena Telenius ◽  
Stephanie J. Carpenter ◽  
Lea Nussbaum ◽  
...  
Keyword(s):  
High Resolution ◽  
Fold Increase ◽  
Chromosome Conformation Capture ◽  
New Method ◽  
Entire Genome ◽  
Chromosome Conformation ◽  
Target Sequencing ◽  
Genome Wide ◽  
Technical Modifications ◽  
Wide Scale

ABSTRACTChromosome conformation capture (3C) provides an adaptable tool for studying diverse biological questions. Current 3C methods provide either low-resolution interaction profiles across the entire genome, or high-resolution interaction profiles at up to several hundred loci. All 3C methods are affected to varying degrees by inefficiency, bias and noise. As such, generation of reproducible high-resolution interaction profiles has not been achieved at scale. To overcome this barrier, we systematically tested and improved upon current methods. We show that isolation of 3C libraries from intact nuclei, as well as shortening and titration of enrichment oligonucleotides used in high-resolution methods reduces noise and increases on-target sequencing. We combined these technical modifications into a new method Nuclear-Titrated (NuTi) Capture-C, which provides a >3-fold increase in informative sequencing content over current Capture-C protocols. Using NuTi Capture-C we target 8,061 promoters in triplicate, demonstrating that this method generates reproducible high-resolution genome-wide 3C interaction profiles at scale.

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