scholarly journals Multiplex Chromatin Interaction Analysis with Single-Molecule Precision

2018 ◽  
Author(s):  
Meizhen Zheng ◽  
Simon Zhongyuan Tian ◽  
Rahul Maurya ◽  
Byoungkoo Lee ◽  
Minji Kim ◽  
...  
Keyword(s):  
Single Molecule ◽  
Interaction Analysis ◽  
Dna Barcode ◽  
Chromatin Interaction ◽  
Gene Promoters ◽  
Single Molecule Level ◽  
Proximity Ligation ◽  
Chromatin Interactions ◽  
Genome Wide ◽  
Topologically Associated Domains

We describe a microfluidics-based strategy for genome-wide analysis of multiplex chromatin interactions with single-molecule precision. In multiplex chromatin interaction analysis (multi-ChIA), individual chromatin complexes are partitioned into droplets that contain a gel bead with unique DNA barcode, in which tethered chromatin DNA fragments are barcoded and amplified for sequencing and mapping to demarcate chromatin contacts. Thus, multi-ChIA has the unprecedented ability to uncover multiplex chromatin interactions at single-molecule level, which has been impossible using previous methods that rely on analyzing pairwise contacts via proximity ligation. We demonstrate that multiplex chromatin interactions predominantly contribute to topologically associated domains, and clusters of gene promoters and enhancers provide a fundamental topological framework for co-transcriptional regulation.

scholarly journals ChIAMM: A Mixture Model for Statistical Analysis of Long-Range Chromatin Interactions From ChIA-PET Experiments

2020 ◽  
Vol 11 ◽  
Author(s):  
Yibeltal Arega ◽  
Hao Jiang ◽  
Shuangqi Wang ◽  
Jingwen Zhang ◽  
Xiaohui Niu ◽  
...  
Keyword(s):  
Mixture Model ◽  
Interaction Analysis ◽  
Gc Content ◽  
Specific Protein ◽  
Chromatin Interaction ◽  
New Approach ◽  
Chromatin Interactions ◽  
Genome Wide ◽  
Systematic Biases ◽  
Local Enrichment

Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is an important experimental method for detecting specific protein-mediated chromatin loops genome-wide at high resolution. Here, we proposed a new statistical approach with a mixture model, chromatin interaction analysis using mixture model (ChIAMM), to detect significant chromatin interactions from ChIA-PET data. The statistical model is cast into a Bayesian framework to consider more systematic biases: the genomic distance, local enrichment, mappability, and GC content. Using different ChIA-PET datasets, we evaluated the performance of ChIAMM and compared it with the existing methods, including ChIA-PET Tool, ChiaSig, Mango, ChIA-PET2, and ChIAPoP. The result showed that the new approach performed better than most top existing methods in detecting significant chromatin interactions in ChIA-PET experiments.

scholarly journals ChIA-PIPE: A fully automated pipeline for comprehensive ChIA-PET data analysis and visualization

2020 ◽  
Vol 6 (28) ◽  
pp. eaay2078 ◽  
Author(s):  
Byoungkoo Lee ◽  
Jiahui Wang ◽  
Liuyang Cai ◽  
Minji Kim ◽  
Sandeep Namburi ◽  
...  
Keyword(s):  
Interaction Analysis ◽  
Specific Protein ◽  
Chromatin Interaction ◽  
Data Types ◽  
Contact Map ◽  
Read Mapping ◽  
Chromatin Interactions ◽  
Genome Wide ◽  
Allele Specific ◽  
Automated Pipeline

ChIA-PET (chromatin interaction analysis with paired-end tags) enables genome-wide discovery of chromatin interactions involving specific protein factors, with base pair resolution. Interpretation of ChIA-PET data requires a robust analytic pipeline. Here, we introduce ChIA-PIPE, a fully automated pipeline for ChIA-PET data processing, quality assessment, visualization, and analysis. ChIA-PIPE performs linker filtering, read mapping, peak calling, and loop calling and automates quality control assessment for each dataset. To enable visualization, ChIA-PIPE generates input files for two-dimensional contact map viewing with Juicebox and HiGlass and provides a new dockerized visualization tool for high-resolution, browser-based exploration of peaks and loops. To enable structural interpretation, ChIA-PIPE calls chromatin contact domains, resolves allele-specific peaks and loops, and annotates enhancer-promoter loops. ChIA-PIPE also supports the analysis of other related chromatin-mapping data types.

scholarly journals MIA-Sig: multiplex chromatin interaction analysis by signal processing and statistical algorithms

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Minji Kim ◽  
Meizhen Zheng ◽  
Simon Zhongyuan Tian ◽  
Byoungkoo Lee ◽  
Jeffrey H. Chuang ◽  
...  
Keyword(s):  
Signal Processing ◽  
Single Molecule ◽  
Genome Mapping ◽  
Interaction Analysis ◽  
Statistical Significance ◽  
Chromatin Interaction ◽  
Topological Domains ◽  
3D Genome ◽  
Algorithmic Solution ◽  
Algorithmic Framework

AbstractThe single-molecule multiplex chromatin interaction data are generated by emerging 3D genome mapping technologies such as GAM, SPRITE, and ChIA-Drop. These datasets provide insights into high-dimensional chromatin organization, yet introduce new computational challenges. Thus, we developed MIA-Sig, an algorithmic solution based on signal processing and information theory. We demonstrate its ability to de-noise the multiplex data, assess the statistical significance of chromatin complexes, and identify topological domains and frequent inter-domain contacts. On chromatin immunoprecipitation (ChIP)-enriched data, MIA-Sig can clearly distinguish the protein-associated interactions from the non-specific topological domains. Together, MIA-Sig represents a novel algorithmic framework for multiplex chromatin interaction analysis.

scholarly journals A Bayesian mixture model for chromatin interaction data

2015 ◽  
Vol 14 (1) ◽  
Author(s):  
Liang Niu ◽  
Shili Lin
Keyword(s):  
Protein Binding ◽  
Binding Sites ◽  
Chromatin Interaction ◽  
Molecular Technique ◽  
Type I ◽  
Gene Promoters ◽  
Dna Fragments ◽  
Modeling Framework ◽  
Protein Binding Sites ◽  
Chromatin Interactions

AbstractChromatin interactions mediated by a particular protein are of interest for studying gene regulation, especially the regulation of genes that are associated with, or known to be causative of, a disease. A recent molecular technique, Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET), that uses chromatin immunoprecipitation (ChIP) and high throughput paired-end sequencing, is able to detect such chromatin interactions genomewide. However, ChIA-PET may generate noise (i.e., pairings of DNA fragments by random chance) in addition to true signal (i.e., pairings of DNA fragments by interactions). In this paper, we propose MC_DIST based on a mixture modeling framework to identify true chromatin interactions from ChIA-PET count data (counts of DNA fragment pairs). The model is cast into a Bayesian framework to take into account the dependency among the data and the available information on protein binding sites and gene promoters to reduce false positives. A simulation study showed that MC_DIST outperforms the previously proposed hypergeometric model in terms of both power and type I error rate. A real data study showed that MC_DIST may identify potential chromatin interactions between protein binding sites and gene promoters that may be missed by the hypergeometric model. An R package implementing the MC_DIST model is available at

scholarly journals RedChIP identifies noncoding RNAs associated with genomic sites occupied by Polycomb and CTCF proteins

2021 ◽  
Vol 119 (1) ◽  
pp. e2116222119
Author(s):  
Alexey A. Gavrilov ◽  
Rinat I. Sultanov ◽  
Mikhail D. Magnitov ◽  
Aleksandra A. Galitsyna ◽  
Erdem B. Dashinimaev ◽  
...  
Keyword(s):  
Chromatin Immunoprecipitation ◽  
Binding Sites ◽  
Wide Spectrum ◽  
Noncoding Rnas ◽  
Ctcf Binding ◽  
Polycomb Repressive Complex 2 ◽  
Proximity Ligation ◽  
Chromatin Interactions ◽  
Genome Wide ◽  
Architectural Protein

Nuclear noncoding RNAs (ncRNAs) are key regulators of gene expression and chromatin organization. The progress in studying nuclear ncRNAs depends on the ability to identify the genome-wide spectrum of contacts of ncRNAs with chromatin. To address this question, a panel of RNA–DNA proximity ligation techniques has been developed. However, neither of these techniques examines proteins involved in RNA–chromatin interactions. Here, we introduce RedChIP, a technique combining RNA–DNA proximity ligation and chromatin immunoprecipitation for identifying RNA–chromatin interactions mediated by a particular protein. Using antibodies against architectural protein CTCF and the EZH2 subunit of the Polycomb repressive complex 2, we identify a spectrum of cis- and trans-acting ncRNAs enriched at Polycomb- and CTCF-binding sites in human cells, which may be involved in Polycomb-mediated gene repression and CTCF-dependent chromatin looping. By providing a protein-centric view of RNA–DNA interactions, RedChIP represents an important tool for studies of nuclear ncRNAs.

scholarly journals Multiplex chromatin interaction analysis by signal processing and statistical algorithms

2019 ◽  
Author(s):  
Minji Kim ◽  
Meizhen Zheng ◽  
Simon Zhongyuan Tian ◽  
Daniel Capurso ◽  
Byoungkoo Lee ◽  
...  
Keyword(s):  
Single Molecule ◽  
Chromatin Immunoprecipitation ◽  
Genome Mapping ◽  
Interaction Analysis ◽  
Statistical Significance ◽  
Chromatin Interaction ◽  
Interaction Data ◽  
Topological Domains ◽  
3D Genome ◽  
Algorithmic Framework

AbstractThe single-molecule multiplex chromatin interaction data generated by emerging non-ligation-based 3D genome mapping technologies provide novel insights into high dimensional chromatin organization, yet introduce new computational challenges. We developed MIA-Sig (https://github.com/TheJacksonLaboratory/mia-sig.git), an algorithmic framework to de-noise the data, assess the statistical significance of chromatin complexes, and identify topological domains and inter-domain contacts. On chromatin immunoprecipitation (ChIP)-enriched data, MIA-Sig can clearly distinguish the protein-associated interactions from the non-specific topological domains.

scholarly journals Chromatin interaction analysis with updated ChIA-PET Tool (V3)

2019 ◽  
Author(s):  
Guoliang Li ◽  
Tongkai Sun ◽  
Huidan Chang ◽  
Liuyang Cai ◽  
Ping Hong ◽  
...  
Keyword(s):  
Data Analysis ◽  
Target Genes ◽  
Sequence Data ◽  
Interaction Analysis ◽  
Regulatory Elements ◽  
Chromatin Interaction ◽  
Data Set ◽  
Chromatin Interactions ◽  
A Genome ◽  
Public Data

AbstractUnderstanding chromatin interactions is important since they create chromosome conformation and link the cis- and trans-regulatory elements to their target genes for transcriptional regulation. Chromatin Interaction Analysis with Paired-End Tag (ChIA-PET) sequencing is a genome-wide high-throughput technology that detects chromatin interactions associated with a specific protein of interest. Previously we developed ChIA-PET Tool in 2010 for ChIA-PET data analysis. Here we present the updated version of ChIA-PET Tool (V3), is a computational package to process the next-generation sequence data generated from ChIA-PET experiments. It processes the short-read data and long-read ChIA-PET data with multithreading and generates the statistics of results in a HTML file. In this paper, we provide a detailed demonstration of the design of ChIA-PET Tool V3 and how to install it and analyze a specific ChIA-PET data set with it. At present, other ChIA-PET data analysis tools have developed including ChiaSig, MICC, Mango and ChIA-PET2 and so on. We compared our tool with other tools using the same public data set in the same machine. Most of peaks detected by ChIA-PET Tool V3 overlap with those from other tools. There is higher enrichment for significant chromatin interactions of ChIA-PET Tool V3 in APA plot. ChIA-PET Tool V3 is open source and is available at GitHub (https://github.com/GuoliangLi-HZAU/ChIA-PET_Tool_V3/).

scholarly journals MAPPING OF LONG-RANGE CHROMATIN INTERACTIONS BY PROXIMITY LIGATION ASSISTED CHIP-SEQ

2016 ◽  
Author(s):  
Rongxin Fang ◽  
Miao Yu ◽  
Guoqiang Li ◽  
Sora Chee ◽  
Tristin Liu ◽  
...  
Keyword(s):  
Chromatin Immunoprecipitation ◽  
Embryonic Stem ◽  
Cost Effective ◽  
Eukaryotic Cells ◽  
Proximity Ligation ◽  
Chromatin Interactions ◽  
Genome Wide ◽  
Cost Effective Method ◽  
Highly Sensitive ◽  
State Of Art

AbstractWe report a highly sensitive and cost-effective method for genome-wide identification of chromatin interactions in eukaryotic cells. Combining proximity ligation with chromatin immunoprecipitation and sequencing, the method outperforms the state of art approach in sensitivity, accuracy and ease of operation. Application of the method to mouse embryonic stem cells improves mapping of enhancer-promoter interactions.

scholarly journals Comparison of the 3D organization of sperm and fibroblast genomes using the Hi-C approach

2014 ◽  
Author(s):  
Nariman Battulin ◽  
Veniamin S Fishman ◽  
Alexander M Mazur ◽  
Mikhail Pomaznoy ◽  
Anna A Khabarova ◽  
...  
Keyword(s):  
Es Cells ◽  
3D Structure ◽  
Embryonic Stem ◽  
Cell Types ◽  
Chromatin Interaction ◽  
Sperm Cells ◽  
3D Genome ◽  
Genome Wide ◽  
Topologically Associated Domains ◽  
The Stability

The 3D organization of the genome is tightly connected to its biological function. The Hi-C approach was recently introduced as a method that can be used to identify higher-order chromatin interactions genome-wide. The aim of this study was to determine genome-wide chromatin interaction frequencies using the Hi-C approach in mouse sperm cells and embryonic fibroblasts. The obtained results demonstrated that the 3D genome organizations of sperm and fibroblast cells show a high degree of similarity both with each other and with the previously described mouse embryonic stem (ES) cells. Both A- and B-compartments and topologically associated domains (TADs) are present in spermatozoa and fibroblasts. Nevertheless, sperm cells and fibroblasts exhibited statistically significant differences between each other in the contact probabilities of defined loci. Tight packaging of the sperm genome resulted in an enrichment of long-range contacts compared with the fibroblasts. However, only 30% of the differences in the number of contacts are based on differences in the densities of their genome packages; the main source of the differences is the gain or loss of contacts that are specific for defined genome regions. An analysis of interchromosomal contacts in both cell types demonstrated that the large chromosomes showed a tendency to interact with each other more than with the small chromosomes and vice versa. We found that the dependence of the contact probability P(s) on genomic distance for sperm is in a good agreement with the fractal globular folding of chromatin. The similarity of the spatial DNA organization in sperm and somatic cell genomes suggests the stability of the 3D structure of genomes through generations.

scholarly journals Single molecule-level detection and long read-based phasing of epigenetic variations in bacterial methylomes

2014 ◽  
Author(s):  
John Beaulaurier ◽  
Shijia Zhu ◽  
Robert Sebra ◽  
Xue-Song Zhang ◽  
Chaggai Rosenbluh ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Molecule ◽  
Population Level ◽  
Bacterial Strains ◽  
Smrt Sequencing ◽  
Bacterial Populations ◽  
Single Molecule Level ◽  
Genome Wide ◽  
A Genome ◽  
Long Read

Comprehensive genome-wide analyses of bacterial DNA methylation have not been possible until the recent advent of single molecule, real-time (SMRT) sequencing. This technology enables the direct detection of N6-methyladenine (6mA) and 4-methylcytosine (4mC) at single nucleotide resolution on a genome-wide scale. The distributions of these two major types of DNA methylation, along with 5-methylcytosine (5mC), comprise the bacterial methylome, some rare exceptions notwithstanding. SMRT sequencing has already revealed marked diversity in bacterial methylomes as well as the existence of heterogeneity of methylation in cells in single bacterial colonies, where such ‘epigenetic’ variation can enable bacterial populations to rapidly adapt to changing conditions. However, current methods for studying bacterial methylomes using SMRT sequencing mainly rely on population-level summaries that do not provide the single-cell resolution necessary for dissecting the epigenetic heterogeneity in bacterial populations. Here, we present a novel SMRT sequencing-based framework, consisting of two complementary methods, for single molecule-level detection of DNA methylation and assessment of methyltransferase activity through single molecule-level long read-based epigenetic phasing. Using seven bacterial strains and integrating data from SMRT and Illumina sequencing, we show that our method yields significantly improved resolution compared to existing population-level methods, and reveals several distinct types of epigenetic heterogeneity. Our approach enables new investigations of the complex architecture and dynamics of bacterial methylomes and provides a powerful new tool for the study of bacterial epigenetic control.

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