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 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 Genome-wide map of proximity linkage to renin proximal promoter in rat

2018 ◽  
Vol 50 (5) ◽  
pp. 323-331 ◽  
Author(s):  
Timothy J. Stodola ◽  
Pengyuan Liu ◽  
Yong Liu ◽  
Andrew K. Vallejos ◽  
Aron M. Geurts ◽  
...  
Keyword(s):  
Plasma Renin ◽  
Proximal Promoter ◽  
Chromosome X ◽  
Genome Wide ◽  
A Genome ◽  
New Directions ◽  
Renin Gene ◽  
Capture Method ◽  
Genomic Regions

A challenge to understanding enhancer-gene relationships is that enhancers are not always sequentially close to the gene they regulate. Physical proximity mapping through sequencing can provide an unbiased view of the chromatin close to the proximal promoter of the renin gene ( Ren). Our objective was to determine genomic regions that physically interact with the renin proximal promoter, using two different genetic backgrounds, the Dahl salt sensitive and normotensive SS-13BN, which have been shown to have different regulation of plasma renin in vivo. The chromatin conformation capture method with sequencing focused at the Ren proximal promoter in rat-derived cardiac endothelial cells was used. Cells were fixed, chromatin close to the Ren promoter was captured, and fragments were sequenced. The clustering of mapped reads produced a genome-wide map of chromatin in contact with the Ren promoter. The largest number of contacts was found on chromosome 13, the chromosome with Ren, and contacts were found on all other chromosomes except chromosome X. These contacts were significantly enriched with genes positively correlated with Ren expression and with mapped quantitative trait loci associated with blood pressure, cardiovascular, and renal phenotypes. The results were reproducible in an independent biological replicate. The findings reported here represent the first map between a critical cardiovascular gene and physical interacting loci throughout the genome and will provide the basis for several new directions of research.

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 RUNX1-mediated alphaherpesvirus-host trans-species chromatin interaction promotes viral transcription

2021 ◽  
Vol 7 (26) ◽  
pp. eabf8962
Author(s):  
Ke Xiao ◽  
Dan Xiong ◽  
Gong Chen ◽  
Jinsong Yu ◽  
Yue Li ◽  
...  
Keyword(s):  
Pseudorabies Virus ◽  
Host Cells ◽  
Chromatin Interaction ◽  
Open Chromatin ◽  
Chromosome Conformation ◽  
Dna Viruses ◽  
Genome Wide ◽  
A Genome ◽  
Genome Transcription ◽  
Active Transcription

Like most DNA viruses, herpesviruses precisely deliver their genomes into the sophisticatedly organized nuclei of the infected host cells to initiate subsequent transcription and replication. However, it remains elusive how the viral genome specifically interacts with the host genome and hijacks host transcription machinery. Using pseudorabies virus (PRV) as model virus, we performed chromosome conformation capture assays to demonstrate a genome-wide specific trans-species chromatin interaction between the virus and host. Our data show that the PRV genome is delivered by the host DNA binding protein RUNX1 into the open chromatin and active transcription zone. This facilitates virus hijacking host RNAPII to efficiently transcribe viral genes, which is significantly inhibited by either a RUNX1 inhibitor or RNA interference. Together, these findings provide insights into the chromatin interaction between viral and host genomes and identify new areas of research to advance the understanding of herpesvirus genome transcription.

scholarly journals A supervised learning framework for chromatin loop detection in genome-wide contact maps

2019 ◽  
Author(s):  
Tarik J. Salameh ◽  
Xiaotao Wang ◽  
Fan Song ◽  
Bo Zhang ◽  
Sage M. Wright ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Data Types ◽  
Random Forest Classification ◽  
Chromatin Loops ◽  
3D Genome ◽  
Contact Maps ◽  
Genome Wide ◽  
A Genome ◽  
Loop Detection ◽  
Wide Contact

ABSTRACTAccurately predicting chromatin loops from genome-wide interaction matrices such as Hi-C data is critical to deepen our understanding of proper gene regulation events. Current approaches are mainly focused on searching for statistically enriched dots on a genome-wide map. However, given the availability of a wide variety of orthogonal data types such as ChIA-PET, GAM, SPRITE, and high-throughput imaging, a supervised learning approach could facilitate the discovery of a comprehensive set of chromatin interactions. Here we present Peakachu, a Random Forest classification framework that predicts chromatin loops from genome-wide contact maps. Compared with current enrichment-based approaches, Peakachu identified more meaningful short-range interactions. We show that our models perform well in different platforms such as Hi-C, Micro-C, and DNA SPRITE, across different sequencing depths, and across different species. We applied this framework to systematically predict chromatin loops in 56 Hi-C datasets, and the results are available at the 3D Genome Browser (www.3dgenome.org).

scholarly journals GenomeDISCO: A concordance score for chromosome conformation capture experiments using random walks on contact map graphs

2017 ◽  
Author(s):  
Oana Ursu ◽  
Nathan Boley ◽  
Maryna Taranova ◽  
Y.X. Rachel Wang ◽  
Galip Gurkan Yardimci ◽  
...  
Keyword(s):  
Random Walks ◽  
Critical Role ◽  
Three Dimensional ◽  
Cell Types ◽  
Chromosome Conformation Capture ◽  
Supplementary Information ◽  
Contact Map ◽  
Chromosome Conformation ◽  
Contact Maps ◽  
Map Graphs

AbstractMotivationThe three-dimensional organization of chromatin plays a critical role in gene regulation and disease. High-throughput chromosome conformation capture experiments such as Hi-C are used to obtain genome-wide maps of 3D chromatin contacts. However, robust estimation of data quality and systematic comparison of these contact maps is challenging due to the multi-scale, hierarchical structure of chromatin contacts and the resulting properties of experimental noise in the data. Measuring concordance of contact maps is important for assessing reproducibility of replicate experiments and for modeling variation between different cellular contexts.ResultsWe introduce a concordance measure called GenomeDISCO (DIfferences between Smoothed COntact maps) for assessing the similarity of a pair of contact maps obtained from chromosome conformation capture experiments. The key idea is to smooth contact maps using random walks on the contact map graph, before estimating concordance. We use simulated datasets to benchmark GenomeDISCO’s sensitivity to different types of noise that affect chromatin contact maps. When applied to a large collection of Hi-C datasets, GenomeDISCO accurately distinguishes biological replicates from samples obtained from different cell types. GenomeDISCO also generalizes to other chromosome conformation capture assays, such as HiChIP.AvailabilitySoftware implementing GenomeDISCO is available at https://github.com/kundajelab/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals MaxHiC: robust estimation of chromatin interaction frequency in Hi-C and capture Hi-C experiments

2020 ◽  
Author(s):  
Hamid Alinejad-Rokny ◽  
Rassa Ghavami ◽  
Hamid R. Rabiee ◽  
Narges Rezaei ◽  
Kin Tung Tam ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Gc Content ◽  
Genomic Region ◽  
Background Correction ◽  
Ctcf Binding ◽  
Chromatin Interaction ◽  
Distribution Model ◽  
Chromosome Conformation ◽  
Genome Wide ◽  
A Genome

AbstractHi-C is a genome-wide chromosome conformation capture technology that detects interactions between pairs of genomic regions, and exploits higher order chromatin structures. Conceptually Hi-C data counts interaction frequencies between every position in the genome and every other position. Biologically functional interactions are expected to occur more frequently than random (background) interactions. To identify biologically relevant interactions, several background models that take biases such as distance, GC content and mappability into account have been proposed. Here we introduce MaxHiC, a background correction tool that deals with these complex biases and robustly identifies statistically significant interactions in both Hi-C and capture Hi-C experiments. MaxHiC uses a negative binomial distribution model and a maximum likelihood technique to correct biases in both Hi-C and capture Hi-C libraries. We systematically benchmark MaxHiC against major Hi-C background correction tools and demonstrate using published Hi-C and capture Hi-C datasets that 1) Interacting regions identified by MaxHiC have significantly greater levels of overlap with known regulatory features (e.g. active chromatin histone marks, CTCF binding sites, DNase sensitivity) and also disease-associated genome-wide association SNPs than those identified by currently existing models, and 2) the pairs of interacting regions are more likely to be linked by eQTL pairs and more likely to identify known enhancer-promoter pairs than any of the existing methods. We also demonstrate that interactions between different genomic region types have distinct distance distribution only revealed by MaxHiC. MaxHiC is publicly available as a python package for the analysis of Hi-C and capture Hi-C data.

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.

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