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 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.

The 3D Genome Structure of Single Cells

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Tianming Zhou ◽  
Ruochi Zhang ◽  
Jian Ma
Keyword(s):  
Single Cell ◽  
Data Science ◽  
Spatial Organization ◽  
Method Development ◽  
Single Cells ◽  
Genome Structure ◽  
Computational Method ◽  
Publication Date ◽  
3D Genome ◽  
Genome Features

The spatial organization of the genome in the cell nucleus is pivotal to cell function. However, how the 3D genome organization and its dynamics influence cellular phenotypes remains poorly understood. The very recent development of single-cell technologies for probing the 3D genome, especially single-cell Hi-C (scHi-C), has ushered in a new era of unveiling cell-to-cell variability of 3D genome features at an unprecedented resolution. Here, we review recent developments in computational approaches to the analysis of scHi-C, including data processing, dimensionality reduction, imputation for enhancing data quality, and the revealing of 3D genome features at single-cell resolution. While much progress has been made in computational method development to analyze single-cell 3D genomes, substantial future work is needed to improve data interpretation and multimodal data integration, which are critical to reveal fundamental connections between genome structure and function among heterogeneous cell populations in various biological contexts. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 4 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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 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 SPIN reveals genome-wide landscape of nuclear compartmentalization

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yuchuan Wang ◽  
Yang Zhang ◽  
Ruochi Zhang ◽  
Tom van Schaik ◽  
Liguo Zhang ◽  
...  
Keyword(s):  
Spatial Organization ◽  
K562 Cells ◽  
Replication Timing ◽  
Computational Method ◽  
Chromatin Interaction ◽  
Nuclear Speckles ◽  
Genome Spatial Organization ◽  
3D Genome ◽  
Genome Wide ◽  
Nuclear Compartmentalization

AbstractWe report SPIN, an integrative computational method to reveal genome-wide intranuclear chromosome positioning and nuclear compartmentalization relative to multiple nuclear structures, which are pivotal for modulating genome function. As a proof-of-principle, we use SPIN to integrate nuclear compartment mapping (TSA-seq and DamID) and chromatin interaction data (Hi-C) from K562 cells to identify 10 spatial compartmentalization states genome-wide relative to nuclear speckles, lamina, and putative associations with nucleoli. These SPIN states show novel patterns of genome spatial organization and their relation to other 3D genome features and genome function (transcription and replication timing). SPIN provides critical insights into nuclear spatial and functional compartmentalization.

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 DeepC: Predicting chromatin interactions using megabase scaled deep neural networks and transfer learning

2019 ◽  
Author(s):  
Ron Schwessinger ◽  
Matthew Gosden ◽  
Damien Downes ◽  
Richard Brown ◽  
Jelena Telenius ◽  
...  
Keyword(s):  
Dna Sequence ◽  
Large Scale ◽  
Genome Structure ◽  
Chromatin Interaction ◽  
3D Genome ◽  
Chromatin Interactions ◽  
Genome Wide ◽  
Chromatin Folding ◽  
The Impact ◽  
3D Genome Structure

AbstractUnderstanding 3D genome structure requires high throughput, genome-wide approaches. However, assays for all vs. all chromatin interaction mapping are expensive and time consuming, which severely restricts their usage for large-scale mutagenesis screens or for mapping the impact of sequence variants. Computational models sophisticated enough to grasp the determinants of chromatin folding provide a unique window into the functional determinants of 3D genome structure as well as the effects of genome variation.A chromatin interaction predictor should work at the base pair level but also incorporate large-scale genomic context to simultaneously capture the large scale and intricate structures of chromatin architecture. Similarly, to be a flexible and generalisable approach it should also be applicable to data it has not been explicitly trained on. To develop a model with these properties, we designed a deep neuronal network (deepC) that utilizes transfer learning to accurately predict chromatin interactions from DNA sequence at megabase scale. The model generalizes well to unseen chromosomes and works across cell types, Hi-C data resolutions and a range of sequencing depths. DeepC integrates DNA sequence context on an unprecedented scale, bridging the different levels of resolution from base pairs to TADs. We demonstrate how this model allows us to investigate sequence determinants of chromatin folding at genome-wide scale and to predict the importance of regulatory elements and the impact of sequence variations.

scholarly journals Systematic evaluation of chromosome conformation capture assays

2020 ◽  
Author(s):  
Betul Akgol Oksuz ◽  
Liyan Yang ◽  
Sameer Abraham ◽  
Sergey V. Venev ◽  
Nils Krietenstein ◽  
...  
Keyword(s):  
Spatial Organization ◽  
Cell Types ◽  
Chromosome Conformation Capture ◽  
Chromatin Interaction ◽  
Systematic Evaluation ◽  
Chromosome Conformation ◽  
3D Genome ◽  
Experimental Parameters ◽  
Genome Features ◽  
Multiple Cell

AbstractChromosome conformation capture (3C)-based assays are used to map chromatin interactions genome-wide. Quantitative analyses of chromatin interaction maps can lead to insights into the spatial organization of chromosomes and the mechanisms by which they fold. A number of protocols such as in situ Hi-C and Micro-C are now widely used and these differ in key experimental parameters including cross-linking chemistry and chromatin fragmentation strategy. To understand how the choice of experimental protocol determines the ability to detect and quantify aspects of chromosome folding we have performed a systematic evaluation of experimental parameters of 3C-based protocols. We find that different protocols capture different 3D genome features with different efficiencies. First, the use of cross-linkers such as DSG in addition to formaldehyde improves signal-to-noise allowing detection of thousands of additional loops and strengthens the compartment signal. Second, fragmenting chromatin to the level of nucleosomes using MNase allows detection of more loops. On the other hand, protocols that generate larger multi-kb fragments produce stronger compartmentalization signals. We confirmed our results for multiple cell types and cell cycle stages. We find that cell type-specific quantitative differences in chromosome folding are not detected or underestimated by some protocols. Based on these insights we developed Hi-C 3.0, a single protocol that can be used to both efficiently detect chromatin loops and to quantify compartmentalization. Finally, this study produced ultra-deeply sequenced reference interaction maps using conventional Hi-C, Micro-C and Hi-C 3.0 for commonly used cell lines in the 4D Nucleome Project.

scholarly journals L1 and B1 repeats blueprint the spatial organization of chromatin

2019 ◽  
Author(s):  
J. Yuyang Lu ◽  
Lei Chang ◽  
Tong Li ◽  
Ting Wang ◽  
Yafei Yin ◽  
...  
Keyword(s):  
Dna Sequences ◽  
Mammalian Cells ◽  
Spatial Organization ◽  
Genome Structure ◽  
Three Dimensional ◽  
Order Structure ◽  
Basic Principles ◽  
Heterochromatin Protein ◽  
3D Genome ◽  
Genomic Repeats

SUMMARYDespite extensive mapping of three-dimensional (3D) chromatin structures, the basic principles underlying genome folding remain unknown. Here, we report a fundamental role for L1 and B1 retrotransposons in shaping the macroscopic 3D genome structure. Homotypic clustering of B1 and L1 repeats in the nuclear interior or at the nuclear and nucleolar peripheries, respectively, segregates the genome into mutually exclusive nuclear compartments. This spatial segregation of L1 and B1 is conserved in mouse and human cells, and occurs dynamically during establishment of the 3D chromatin structure in early embryogenesis and the cell cycle. Depletion of L1 transcripts drastically disrupts the spatial distributions of L1- and B1-rich compartments. L1 transcripts are strongly associated with L1 DNA sequences and induce phase separation of the heterochromatin protein HP1α. Our results suggest that genomic repeats act as the blueprint of chromatin macrostructure, thus explaining the conserved higher-order structure of chromatin across mammalian cells.

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