scholarly journals Bayesian inference of chromatin structure ensembles from population Hi-C data

2018 ◽  
Author(s):  
Simeon Carstens ◽  
Michael Nilges ◽  
Michael Habeck
Keyword(s):  
Bayesian Inference ◽  
High Throughput ◽  
Chromatin Structure ◽  
Genomic Structure ◽  
Large Population ◽  
Chromosome Conformation Capture ◽  
Chromosome Conformation ◽  
Cell To Cell Variability ◽  
Rigorous Method

High-throughput chromosome conformation capture (Hi-C) experiments are typically performed on a large population of cells and therefore only yield average numbers of genomic contacts. Nevertheless population Hi-C data are often interpreted in terms of a single genomic structure, which ignores all cell-to-cell variability. We propose a probabilistic, statistically rigorous method to infer chromatin structure ensembles from population Hi-C data that takes the ensemble nature of the data explicitly into account and allows us to infer the number of structures required to explain the data.

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 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 Chromosome Conformation Capture with Nuclear Titrated Capture-C (NuTi Capture-C)

2021 ◽  
Author(s):  
Damien J. Downes ◽  
Jim R. Hughes
Keyword(s):  
High Resolution ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Chromosome Conformation Capture ◽  
Target Sequence ◽  
Sequence Specificity ◽  
Chromosome Conformation ◽  
Chromatin Interactions

Abstract NuTi Capture-C is a Chromosome Conformation Capture (3C) approach, which can very efficiently identify chromatin interactions at target viewpoints at high resolution. The addition of high-throughput sequencing adaptors prior to enrichment allows for multiplexing of replicates and comparison samples. This method is an improvement on the previous NG Capture-C1 method in that modifications have been made to the in situ 3C method to improve nuclear integrity (Nuclear 3C). Additionally, capture has been optimised to viewpoint complexity through titration, maximising on target sequence specificity. The experiment will take several weeks and provide reproducible interaction profiles for tens to thousands of viewpoints of interest.

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