scholarly journals 5C-ID: Increased resolution Chromosome-Conformation-Capture-Carbon-Copy with in situ 3C and double alternating primer design

2018 ◽  
Author(s):  
Ji Hun Kim ◽  
Katelyn R. Titus ◽  
Wanfeng Gong ◽  
Jonathan A. Beagan ◽  
Zhendong Cao ◽  
...  
Keyword(s):  
High Resolution ◽  
High Throughput Screening ◽  
Chromosome Conformation Capture ◽  
High Coverage ◽  
Chromosome Conformation ◽  
3D Genome ◽  
Loop Detection ◽  
Need To Evaluate ◽  
Carbon Copy

AbstractMammalian genomes are folded in a hierarchy of compartments, topologically associating domains (TADs), subTADs and looping interactions. Currently, there is a great need to evaluate the link between chromatin topology and genome function across many biological conditions and genetic perturbations. Hi-C generates high quality, high resolution maps of looping interactions genome-wide, but is intractable for high-throughput screening of loops across conditions due to the requirement of an enormous number of reads (>6 Billion) per library. Here, we describe 5C-ID, an updated version of Chromosome-Conformation-Capture-Carbon-Copy (5C) with restriction digest and ligation performed in the nucleus (in situ Chromosome-Conformation-Capture (3C)) and ligation-mediated amplification performed with a new double alternating design. 5C-ID reduces spatial noise and enables higher resolution 3D genome folding maps than canonical 5C, allowing for a marked improvement in sensitivity and specificity of loop detection. 5C-ID enables the creation of high-resolution, high-coverage maps of chromatin loops in up to a 30 Megabase subset of the genome at a fraction of the cost of Hi-C.

scholarly journals Systematic evaluation of statistical methods for identifying looping interactions in 5C data

2017 ◽  
Author(s):  
Thomas G. Gilgenast ◽  
Jennifer E. Phillips-Cremins
Keyword(s):  
High Resolution ◽  
Statistical Methods ◽  
Variance Estimation ◽  
Chromosome Conformation Capture ◽  
Systematic Evaluation ◽  
High Coverage ◽  
Method Performance ◽  
Chromosome Conformation ◽  
Trade Offs ◽  
Carbon Copy

SummaryChromosome-Conformation-Capture-Carbon-Copy (5C) is a molecular technology based on proximity ligation that enables high-resolution and high-coverage inquiry of long-range chromatin looping interactions. Computational pipelines for analyzing 5C data involve a series of inter-dependent normalization procedures and statistical methods that markedly influence downstream biological results. A detailed analysis of the trade-offs inherent to all stages of 5C analysis has not been reported, but is essential for understanding the biological basis of looping. Here, we provide a comparative assessment of method performance at each step in the 5C analysis pipeline, including sequencing depth and library complexity correction, bias mitigation, spatial noise reduction, distance-dependent expected and variance estimation, modeling, and loop detection. We present a detailed discussion of methodological advantages/disadvantages at each step and provide a full suite of algorithms, lib5C, to allow investigators to test the range of approaches on their own high-resolution 5C data. Principles learned from our comparative analyses will have broad impact on many other forms of Chromosome-Conformation-Capture-based data, including Hi-C, 4C, and Capture-C.

scholarly journals 5C-ID: Increased resolution Chromosome-Conformation-Capture-Carbon-Copy with in situ 3C and double alternating primer design

Methods ◽  
2018 ◽  
Vol 142 ◽  
pp. 39-46 ◽  
Author(s):  
Ji Hun Kim ◽  
Katelyn R. Titus ◽  
Wanfeng Gong ◽  
Jonathan A. Beagan ◽  
Zhendong Cao ◽  
...  
Keyword(s):  
Primer Design ◽  
Chromosome Conformation Capture ◽  
Chromosome Conformation ◽  
Carbon Copy

scholarly journals Computational methods for predicting 3D genomic organization from high-resolution chromosome conformation capture data

2020 ◽  
Vol 19 (4) ◽  
pp. 292-308 ◽  
Author(s):  
Kimberly MacKay ◽  
Anthony Kusalik
Keyword(s):  
High Resolution ◽  
Genome Organization ◽  
3D Structure ◽  
Likelihood Estimation ◽  
Genomic Region ◽  
Chromosome Conformation Capture ◽  
Computational Tools ◽  
Chromosome Conformation ◽  
Single Chromosome ◽  
3D Genome

Abstract The advent of high-resolution chromosome conformation capture assays (such as 5C, Hi-C and Pore-C) has allowed for unprecedented sequence-level investigations into the structure–function relationship of the genome. In order to comprehensively understand this relationship, computational tools are required that utilize data generated from these assays to predict 3D genome organization (the 3D genome reconstruction problem). Many computational tools have been developed that answer this need, but a comprehensive comparison of their underlying algorithmic approaches has not been conducted. This manuscript provides a comprehensive review of the existing computational tools (from November 2006 to September 2019, inclusive) that can be used to predict 3D genome organizations from high-resolution chromosome conformation capture data. Overall, existing tools were found to use a relatively small set of algorithms from one or more of the following categories: dimensionality reduction, graph/network theory, maximum likelihood estimation (MLE) and statistical modeling. Solutions in each category are far from maturity, and the breadth and depth of various algorithmic categories have not been fully explored. While the tools for predicting 3D structure for a genomic region or single chromosome are diverse, there is a general lack of algorithmic diversity among computational tools for predicting the complete 3D genome organization from high-resolution chromosome conformation capture data.

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.

scholarly journals HP1 drives de novo 3D genome reorganization in early Drosophila embryos

Nature ◽  
2021 ◽  
Author(s):  
Fides Zenk ◽  
Yinxiu Zhan ◽  
Pavel Kos ◽  
Eva Löser ◽  
Nazerke Atinbayeva ◽  
...  
Keyword(s):  
Genome Organization ◽  
Molecular Mechanisms ◽  
High Throughput Sequencing ◽  
De Novo ◽  
Early Embryo ◽  
Heterochromatin Protein ◽  
Chromosome Conformation ◽  
3D Genome ◽  
Genome Reorganization

AbstractFundamental features of 3D genome organization are established de novo in the early embryo, including clustering of pericentromeric regions, the folding of chromosome arms and the segregation of chromosomes into active (A-) and inactive (B-) compartments. However, the molecular mechanisms that drive de novo organization remain unknown1,2. Here, by combining chromosome conformation capture (Hi-C), chromatin immunoprecipitation with high-throughput sequencing (ChIP–seq), 3D DNA fluorescence in situ hybridization (3D DNA FISH) and polymer simulations, we show that heterochromatin protein 1a (HP1a) is essential for de novo 3D genome organization during Drosophila early development. The binding of HP1a at pericentromeric heterochromatin is required to establish clustering of pericentromeric regions. Moreover, HP1a binding within chromosome arms is responsible for overall chromosome folding and has an important role in the formation of B-compartment regions. However, depletion of HP1a does not affect the A-compartment, which suggests that a different molecular mechanism segregates active chromosome regions. Our work identifies HP1a as an epigenetic regulator that is involved in establishing the global structure of the genome in the early embryo.

scholarly journals Order and stochasticity in the folding of individual Drosophila genomes

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sergey V. Ulianov ◽  
Vlada V. Zakharova ◽  
Aleksandra A. Galitsyna ◽  
Pavel I. Kos ◽  
Kirill E. Polovnikov ◽  
...  
Keyword(s):  
Random Walk ◽  
High Resolution ◽  
3D Genome ◽  
Topologically Associating Domains ◽  
Chromatin Folding ◽  
A Cell ◽  
Single Nucleus ◽  
High Level ◽  
Cell To Cell Variability

AbstractMammalian and Drosophila genomes are partitioned into topologically associating domains (TADs). Although this partitioning has been reported to be functionally relevant, it is unclear whether TADs represent true physical units located at the same genomic positions in each cell nucleus or emerge as an average of numerous alternative chromatin folding patterns in a cell population. Here, we use a single-nucleus Hi-C technique to construct high-resolution Hi-C maps in individual Drosophila genomes. These maps demonstrate chromatin compartmentalization at the megabase scale and partitioning of the genome into non-hierarchical TADs at the scale of 100 kb, which closely resembles the TAD profile in the bulk in situ Hi-C data. Over 40% of TAD boundaries are conserved between individual nuclei and possess a high level of active epigenetic marks. Polymer simulations demonstrate that chromatin folding is best described by the random walk model within TADs and is most suitably approximated by a crumpled globule build of Gaussian blobs at longer distances. We observe prominent cell-to-cell variability in the long-range contacts between either active genome loci or between Polycomb-bound regions, suggesting an important contribution of stochastic processes to the formation of the Drosophila 3D genome.

3D genome structure reconstruction from chromosomal contact data

2017 ◽  
Author(s):  
◽  
Tuan Anh Trieu
Keyword(s):  
High Resolution ◽  
Genome Structure ◽  
Cell Types ◽  
3D Models ◽  
Graphic User Interface ◽  
Soft Constraints ◽  
Chromosome Conformation ◽  
3D Genome ◽  
Manual Adjustment ◽  
The Relationship

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Different cell types of an organism have the same DNA sequence, but they can function differently because their difference in 3D organization allows them to express different genes and has different cellular functions. Understanding the 3D organization of the genome is the key to understand functions of the cell. Chromosome conformation capture techniques like Hi-C and TCC that can capture interactions between proximal chromosome fragments have allowed the study of 3D genome organization in high resolution and high through-put. My work focuses on developing computational methods to reconstruct 3D genome structures from Hi-C data. I presented three methods to reconstruct 3D genome and chromosome structures. The first method can build 3D genome models from soft constraints of contacts and non-contacts. This method utilizes the concept of contact and non-contact to reconstruct 3D models without translating interaction frequencies into physical distances. The translation is commonly used by other methods even though it makes a strong assumption about the relationship between interaction frequencies and physical distances. In synthetic dataset, when the relationship was known, my method performed comparably with other methods assuming the relationship. This shows the potential of my method for real Hi-C datasets where the relationship is unknown. The limitation of the method is that it has parameters requiring manual adjustment. I developed the second method to reconstruct 3D genome models. This method utilizes a commonly used function to translate interaction frequencies to physical distances to build 3D models. I proposed a novel way to derive soft constraints to handle inconsistency in the data and to make the method robust. Building 3D models at high resolution is a more challenging problem as the number of constraints is small and the feasible space is larger. I introduced a third method to build 3D chromosome models at high resolution. The method reconstructs models at low resolution and then uses them to guide the reconstruction of models at high resolution. The last part of my work is the development of a comprehensive tool with intuitive graphic user interface to analyze Hi-C data, reconstruct and analyze 3D models.

scholarly journals The DLO Hi-C Tool for Digestion-Ligation-Only Hi-C Chromosome Conformation Capture Data Analysis

Genes ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 289 ◽  
Author(s):  
Ping Hong ◽  
Hao Jiang ◽  
Weize Xu ◽  
Da Lin ◽  
Qian Xu ◽  
...  
Keyword(s):  
Target Genes ◽  
High Efficiency ◽  
New Technology ◽  
Three Dimensional ◽  
Large Data ◽  
Regulatory Elements ◽  
Chromosome Conformation Capture ◽  
Genome Chromosome ◽  
Chromosome Conformation ◽  
3D Genome

It is becoming increasingly important to understand the mechanism of regulatory elements on target genes in long-range genomic distance. 3C (chromosome conformation capture) and its derived methods are now widely applied to investigate three-dimensional (3D) genome organizations and gene regulation. Digestion-ligation-only Hi-C (DLO Hi-C) is a new technology with high efficiency and cost-effectiveness for whole-genome chromosome conformation capture. Here, we introduce the DLO Hi-C tool, a flexible and versatile pipeline for processing DLO Hi-C data from raw sequencing reads to normalized contact maps and for providing quality controls for different steps. It includes more efficient iterative mapping and linker filtering. We applied the DLO Hi-C tool to different DLO Hi-C datasets and demonstrated its ability in processing large data with multithreading. The DLO Hi-C tool is suitable for processing DLO Hi-C and in situ DLO Hi-C datasets. It is convenient and efficient for DLO Hi-C data processing.

Sign in / Sign up

Export Citation Format

Share Document