scholarly journals Population-based 3D genome structure analysis reveals driving forces in spatial genome organization

2016 ◽  
Vol 113 (12) ◽  
pp. E1663-E1672 ◽  
Author(s):  
Harianto Tjong ◽  
Wenyuan Li ◽  
Reza Kalhor ◽  
Chao Dai ◽  
Shengli Hao ◽  
...  
Keyword(s):  
Genome Organization ◽  
Driving Forces ◽  
Genome Structure ◽  
Probabilistic Approach ◽  
Population Based ◽  
3D Genome ◽  
Structural Variability ◽  
Chromatin Interactions ◽  
A Genome ◽  
Spatial Genome Organization

Conformation capture technologies (e.g., Hi-C) chart physical interactions between chromatin regions on a genome-wide scale. However, the structural variability of the genome between cells poses a great challenge to interpreting ensemble-averaged Hi-C data, particularly for long-range and interchromosomal interactions. Here, we present a probabilistic approach for deconvoluting Hi-C data into a model population of distinct diploid 3D genome structures, which facilitates the detection of chromatin interactions likely to co-occur in individual cells. Our approach incorporates the stochastic nature of chromosome conformations and allows a detailed analysis of alternative chromatin structure states. For example, we predict and experimentally confirm the presence of large centromere clusters with distinct chromosome compositions varying between individual cells. The stability of these clusters varies greatly with their chromosome identities. We show that these chromosome-specific clusters can play a key role in the overall chromosome positioning in the nucleus and stabilizing specific chromatin interactions. By explicitly considering genome structural variability, our population-based method provides an important tool for revealing novel insights into the key factors shaping the spatial genome organization.

scholarly journals PGS: a dynamic and automated population-based genome structure software

2017 ◽  
Author(s):  
Nan Hua ◽  
Harianto Tjong ◽  
Hanjun Shin ◽  
Ke Gong ◽  
Xianghong Jasmine Zhou ◽  
...  
Keyword(s):  
High Performance ◽  
Spatial Organization ◽  
Genome Structure ◽  
Probabilistic Approach ◽  
Population Based ◽  
Chromatin Interaction ◽  
3D Analysis ◽  
3D Genome ◽  
A Genome ◽  
Contact Frequency

ABSTRACTHi-C technologies are widely used to investigate the spatial organization of genomes. However, the structural variability of the genome is a great challenge to interpreting ensemble-averaged Hi-C data, particularly for long-range/interchromosomal interactions. We pioneered a probabilistic approach for generating a population of distinct diploid 3D genome structures consistent with all the chromatin-chromatin interaction probabilities from Hi-C experiments. Each structure in the population is a physical model of the genome in 3D. Analysis of these models yields new insights into the causes and the functional properties of the genome’s organization in space and time. We provide a user-friendly software package, called PGS, that runs on local machines and high-performance computing platforms. PGS takes a genome-wide Hi-C contact frequency matrix and produces an ensemble of 3D genome structures entirely consistent with the input. The software automatically generates an analysis report, and also provides tools to extract and analyze the 3D coordinates of specific domains.

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 MyoD is a 3D genome structure organizer for muscle cell identity

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Ruiting Wang ◽  
Fengling Chen ◽  
Qian Chen ◽  
Xin Wan ◽  
Minglei Shi ◽  
...  
Keyword(s):  
Muscle Cell ◽  
Genome Organization ◽  
Genome Structure ◽  
Three Dimensional ◽  
Cell Lineage ◽  
Muscle Cells ◽  
Cell Type ◽  
Cell Identity ◽  
3D Genome ◽  
A Genome

AbstractThe genome exists as an organized, three-dimensional (3D) dynamic architecture, and each cell type has a unique 3D genome organization that determines its cell identity. An unresolved question is how cell type-specific 3D genome structures are established during development. Here, we analyzed 3D genome structures in muscle cells from mice lacking the muscle lineage transcription factor (TF), MyoD, versus wild-type mice. We show that MyoD functions as a “genome organizer” that specifies 3D genome architecture unique to muscle cell development, and that H3K27ac is insufficient for the establishment of MyoD-induced chromatin loops in muscle cells. Moreover, we present evidence that other cell lineage-specific TFs might also exert functional roles in orchestrating lineage-specific 3D genome organization during development.

scholarly journals Heterogeneity and Intrinsic Variation in Spatial Genome Organization

2017 ◽  
Author(s):  
Elizabeth H. Finn ◽  
Gianluca Pegoraro ◽  
Hugo B. Brandão ◽  
Anne-Laure Valton ◽  
Marlies E. Oomen ◽  
...  
Keyword(s):  
Genome Organization ◽  
Single Cells ◽  
Human Fibroblasts ◽  
Intrinsic Variability ◽  
Cell Level ◽  
3D Genome ◽  
3D Space ◽  
A Cell ◽  
Spatial Genome Organization ◽  
Independent Behavior

AbstractThe genome is hierarchically organized in 3D space and its architecture is altered in differentiation, development and disease. Some of the general principles that determine global 3D genome organization have been established. However, the extent and nature of cell-to-cell and cell-intrinsic variability in genome architecture are poorly characterized. Here, we systematically probe the heterogeneity in genome organization in human fibroblasts by combining high-resolution Hi-C datasets and high-throughput genome imaging. Optical mapping of several hundred genome interaction pairs at the single cell level demonstrates low steady-state frequencies of colocalization in the population and independent behavior of individual alleles in single nuclei. Association frequencies are determined by genomic distance, higher-order chromatin architecture and chromatin environment. These observations reveal extensive variability and heterogeneity in genome organization at the level of single cells and alleles and they demonstrate the coexistence of a broad spectrum of chromatin and genome conformations in a cell population.

scholarly journals Targeted mutations on 3D hub loci alter spatial interaction environment

2015 ◽  
Author(s):  
Bo Ding ◽  
Lina Zheng ◽  
David Medovoy ◽  
Wei Wang
Keyword(s):  
Genome Organization ◽  
Spatial Organization ◽  
Spatial Interaction ◽  
Genome Structure ◽  
Chromosomal Structure ◽  
Scale Free ◽  
3D Genome ◽  
Coding Regions ◽  
Gene Coding ◽  
Related Genotype

Many disease-related genotype variations (GVs) reside in non-gene coding regions and the mechanisms of their association with diseases are largely unknown. A possible impact of GVs on disease formation is to alter the spatial organization of chromosome. However, the relationship between GVs and 3D genome structure has not been studied at the chromosome scale. The kilobase resolution of chromosomal structures measured by Hi-C have provided an unprecedented opportunity to tackle this problem. Here we proposed a network-based method to capture global properties of the chromosomal structure. We uncovered that genome organization is scale free and the genomic loci interacting with many other loci in space, termed as hubs, are critical for stabilizing local chromosomal structure. Importantly, we found that cancer-specific GVs target hubs to drastically alter the local chromosomal interactions. These analyses revealed the general principles of 3D genome organization and provided a new direction to pinpoint genotype variations in non-coding regions that are critical for disease formation.

scholarly journals GSDB: a database of 3D chromosome and genome structures reconstructed from Hi-C data

2019 ◽  
Author(s):  
Oluwatosin Oluwadare ◽  
Max Highsmith ◽  
Jianlin Cheng
Keyword(s):  
Structure Prediction ◽  
Biological Activities ◽  
Genome Structure ◽  
3D Structure ◽  
Three Dimensional ◽  
Chromosome Conformation ◽  
3D Genome ◽  
Reconstruction Methods ◽  
A Genome ◽  
Structure Reconstruction

ABSTRACTAdvances in the study of chromosome conformation capture (3C) technologies, such as Hi-C technique - capable of capturing chromosomal interactions in a genome-wide scale - have led to the development of three-dimensional (3D) chromosome and genome structure reconstruction methods from Hi-C data. The 3D genome structure is important because it plays a role in a variety of important biological activities such as DNA replication, gene regulation, genome interaction, and gene expression. In recent years, numerous Hi-C datasets have been generated, and likewise, a number of genome structure construction algorithms have been developed. However, until now, there has been no freely available repository for 3D chromosome structures. In this work, we outline the construction of a novel Genome Structure Database (GSDB) to create a comprehensive repository that contains 3D structures for Hi-C datasets constructed by a variety of 3D structure reconstruction tools. GSDB contains over 50,000 structures constructed by 12 state-of-the-art chromosome and genome structure prediction methods for publicly used Hi-C datasets with varying resolution. The database is useful for the community to study the function of genome from a 3D perspective. GSDB is accessible at http://sysbio.rnet.missouri.edu/3dgenome/GSDB

scholarly journals Sci-Hi-C: a single-cell Hi-C method for mapping 3D genome organization in large number of single cells

2019 ◽  
Author(s):  
Vijay Ramani ◽  
Xinxian Deng ◽  
Ruolan Qiu ◽  
Choli Lee ◽  
Christine M Disteche ◽  
...  
Keyword(s):  
Single Cell ◽  
Genome Organization ◽  
Single Cells ◽  
Three Dimensional ◽  
Population Based ◽  
Order Structure ◽  
Chromosome Conformation ◽  
3D Genome ◽  
A Cell ◽  
Cell To Cell Variability

AbstractThe highly dynamic nature of chromosome conformation and three-dimensional (3D) genome organization leads to cell-to-cell variability in chromatin interactions within a cell population, even if the cells of the population appear to be functionally homogeneous. Hence, although Hi-C is a powerful tool for mapping 3D genome organization, this heterogeneity of chromosome higher order structure among individual cells limits the interpretive power of population based bulk Hi-C assays. Moreover, single-cell studies have the potential to enable the identification and characterization of rare cell populations or cell subtypes in a heterogeneous population. However, it may require surveying relatively large numbers of single cells to achieve statistically meaningful observations in single-cell studies. By applying combinatorial cellular indexing to chromosome conformation capture, we developed single-cell combinatorial indexed Hi-C (sci-Hi-C), a high throughput method that enables mapping chromatin interactomes in large number of single cells. We demonstrated the use of sci-Hi-C data to separate cells by karytoypic and cell-cycle state differences and to identify cellular variability in mammalian chromosomal conformation. Here, we provide a detailed description of method design and step-by-step working protocols for sci-Hi-C.

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 ChromeBat: A Bio-Inspired Approach to 3D Genome Reconstruction

2021 ◽  
Author(s):  
Brandon Collins ◽  
Philip N. Brown ◽  
Oluwatosin Oluwadare
Keyword(s):  
Genome Structure ◽  
Three Dimensional ◽  
Genomic Analysis ◽  
Bat Algorithm ◽  
Empirical Measure ◽  
Reconstruction Problem ◽  
Genome Reconstruction ◽  
3D Genome ◽  
A Genome ◽  
Contact Data

Background: With the advent of Next Generation Sequencing and the Hi-C experiment, high quality genome-wide contact data is becoming increasingly available. This data represents an empirical measure of how a genome interacts inside the nucleus. Genome conformation is of particular interest as it has been experimentally shown to be a driving force for many genomic functions from regulation to transcription. Thus, the Three-Dimensional Genome Reconstruction Problem seeks to take Hi-C data and produce the complete physical genome structure as it appears in the nucleus for genomic analysis. Results: We propose and develop a novel method to solve the Chromosome and Genome Reconstruction problem based on the Bat Algorithm which we called ChromeBat.We demonstrate on real Hi-C data that ChromeBat is capable of state of the art performance. Additionally, the domain of Genome Reconstruction has been criticized for lacking algorithmic diversity, and the bio-inspired nature of ChromeBat contributes algorithmic diversity to the problem domain. Conclusions: ChromeBat is an effective approach at solving the Genome Reconstruction Problem. The source code and usage guide can be found here: https://github.com/OluwadareLab/ChromeBat.

scholarly journals Impact of 3D genome organization, guided by cohesin and CTCF looping, on sex-biased chromatin interactions and gene expression in mouse liver

2020 ◽  
Author(s):  
Bryan J Matthews ◽  
David J Waxman
Keyword(s):  
Gene Expression ◽  
Sex Differences ◽  
Binding Sites ◽  
Genome Organization ◽  
Mouse Liver ◽  
Ctcf Binding ◽  
Gene Promoters ◽  
3D Genome ◽  
Chromatin Interactions ◽  
The Impact

Abstract Several thousand sex-differential distal enhancers have been identified in mouse liver; however, their links to sex-biased genes and the impact of any sex-differences in nuclear organization and chromatin interactions are unknown. To address these issues, we first characterized 1,847 mouse liver genomic regions showing significant sex differential occupancy by cohesin and CTCF, two key 3D nuclear organizing factors. These sex-differential binding sites were primarily distal to sex-biased genes but rarely generated sex-differential TAD (topologically associating domain) or intra-TAD loop anchors, and were sometimes found in TADs without sex-biased genes. A substantial subset of sex-biased cohesin-non-CTCF binding sites, but not sex-biased cohesin-and-CTCF binding sites, overlapped sex-biased enhancers. Cohesin depletion reduced the expression of male-biased genes with distal, but not proximal, sex-biased enhancers by >10-fold, implicating cohesin in long-range enhancer interactions regulating sex-biased genes. Using circularized chromosome conformation capture-based sequencing (4C-seq), we showed that sex differences in distal sex-biased enhancer - promoter interactions are common. Intra-TAD loops with sex-independent cohesin-and-CTCF anchors conferred sex specificity to chromatin interactions indirectly, by insulating sex-biased enhancer - promoter contacts and by bringing sex-biased genes into closer proximity to sex-biased enhancers. Furthermore, sex-differential chromatin interactions involving sex-biased gene promoters, enhancers, and lncRNAs were associated with sex-biased binding of cohesin and/or CTCF. These studies elucidate how 3D genome organization impacts sex-biased gene expression in a non-reproductive tissue through both direct and indirect effects of cohesin and CTCF looping on distal enhancer interactions with sex-differentially expressed genes.

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