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

2018 ◽  
Author(s):  
Daniel Capurso ◽  
Jiahui Wang ◽  
Simon Zhongyuan Tian ◽  
Liuyang Cai ◽  
Sandeep Namburi ◽  
...  
Keyword(s):  
Specific Protein ◽  
Peak Calling ◽  
Mapping Data ◽  
Read Mapping ◽  
Contact Maps ◽  
Chromatin Interactions ◽  
Genome Wide ◽  
Allele Specific ◽  
Automated Pipeline ◽  
Contact Domain

AbstractChIA-PET enables the genome-wide discovery of chromatin interactions involving specific protein factors, with base-pair resolution. Interpreting ChIA-PET data depends on having a robust analytic pipeline. Here, we introduce ChIA-PIPE, a fully automated pipeline for ChIA-PET data processing, quality assessment, analysis, and visualization. ChIA-PIPE performs linker filtering, read mapping, peak calling, loop calling, chromatin-contact-domain calling, and can resolve allele-specific peaks and loops. ChIA-PIPE also automates quality-control assessment for each dataset. Furthermore, ChIA-PIPE generates input files for visualizing 2D contact maps with Juicebox and HiGlass, and provides a new dockerized visualization tool for high-resolution, browser-based exploration of peaks and loops. With minimal adjusting, ChIA-PIPE can also be suited for the analysis of other related chromatin-mapping data.

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 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 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 ASHIC: hierarchical Bayesian modeling of diploid chromatin contacts and structures

2020 ◽  
Vol 48 (21) ◽  
pp. e123-e123
Author(s):  
Tiantian Ye ◽  
Wenxiu Ma
Keyword(s):  
Bayesian Framework ◽  
Hierarchical Bayesian ◽  
3D Structures ◽  
Chromatin Interactions ◽  
Genome Wide ◽  
Specific Contact ◽  
Rigorous Framework ◽  
Allele Specific ◽  
Fine Resolution ◽  
Low Coverage

Abstract The recently developed Hi-C technique has been widely applied to map genome-wide chromatin interactions. However, current methods for analyzing diploid Hi-C data cannot fully distinguish between homologous chromosomes. Consequently, the existing diploid Hi-C analyses are based on sparse and inaccurate allele-specific contact matrices, which might lead to incorrect modeling of diploid genome architecture. Here we present ASHIC, a hierarchical Bayesian framework to model allele-specific chromatin organizations in diploid genomes. We developed two models under the Bayesian framework: the Poisson-multinomial (ASHIC-PM) model and the zero-inflated Poisson-multinomial (ASHIC-ZIPM) model. The proposed ASHIC methods impute allele-specific contact maps from diploid Hi-C data and simultaneously infer allelic 3D structures. Through simulation studies, we demonstrated that ASHIC methods outperformed existing approaches, especially under low coverage and low SNP density conditions. Additionally, in the analyses of diploid Hi-C datasets in mouse and human, our ASHIC-ZIPM method produced fine-resolution diploid chromatin maps and 3D structures and provided insights into the allelic chromatin organizations and functions. To summarize, our work provides a statistically rigorous framework for investigating fine-scale allele-specific chromatin conformations. The ASHIC software is publicly available at https://github.com/wmalab/ASHIC.

scholarly journals Bromodomain protein BRDT directs ΔNp63 function and super-enhancer activity in a subset of esophageal squamous cell carcinomas

2021 ◽  
Author(s):  
Xin Wang ◽  
Ana P. Kutschat ◽  
Moyuru Yamada ◽  
Evangelos Prokakis ◽  
Patricia Böttcher ◽  
...  
Keyword(s):  
Squamous Cell ◽  
Target Genes ◽  
Experimental Studies ◽  
Specific Protein ◽  
Chromatin Interaction ◽  
Cell Phenotype ◽  
Precision Oncology ◽  
Enhancer Activity ◽  
Genome Wide ◽  
Geographical Regions

AbstractEsophageal squamous cell carcinoma (ESCC) is the predominant subtype of esophageal cancer with a particularly high prevalence in certain geographical regions and a poor prognosis with a 5-year survival rate of 15–25%. Despite numerous studies characterizing the genetic and transcriptomic landscape of ESCC, there are currently no effective targeted therapies. In this study, we used an unbiased screening approach to uncover novel molecular precision oncology targets for ESCC and identified the bromodomain and extraterminal (BET) family member bromodomain testis-specific protein (BRDT) to be uniquely expressed in a subgroup of ESCC. Experimental studies revealed that BRDT expression promotes migration but is dispensable for cell proliferation. Further mechanistic insight was gained through transcriptome analyses, which revealed that BRDT controls the expression of a subset of ΔNp63 target genes. Epigenome and genome-wide occupancy studies, combined with genome-wide chromatin interaction studies, revealed that BRDT colocalizes and interacts with ΔNp63 to drive a unique transcriptional program and modulate cell phenotype. Our data demonstrate that these genomic regions are enriched for super-enhancers that loop to critical ΔNp63 target genes related to the squamous phenotype such as KRT14, FAT2, and PTHLH. Interestingly, BET proteolysis-targeting chimera, MZ1, reversed the activation of these genes. Importantly, we observed a preferential degradation of BRDT by MZ1 compared with BRD2, BRD3, and BRD4. Taken together, these findings reveal a previously unknown function of BRDT in ESCC and provide a proof-of-concept that BRDT may represent a novel therapeutic target in cancer.

scholarly journals Utilizing networks for differential analysis of chromatin interactions

2017 ◽  
Vol 15 (06) ◽  
pp. 1740008 ◽  
Author(s):  
Lu Liu ◽  
Jianhua Ruan
Keyword(s):  
High Throughput Sequencing ◽  
Signal To Noise Ratio ◽  
Chromatin Interaction ◽  
Differential Analysis ◽  
Local Method ◽  
Global Method ◽  
Chromatin Interactions ◽  
Normalization Strategy ◽  
Genome Wide ◽  
Genomic Regions

Chromatin conformation capture with high-throughput sequencing (Hi-C) is a powerful technique to detect genome-wide chromatin interactions. In this paper, we introduce two novel approaches to detect differentially interacting genomic regions between two Hi-C experiments using a network model. To make input data from multiple experiments comparable, we propose a normalization strategy guided by network topological properties. We then devise two measurements, using local and global connectivity information from the chromatin interaction networks, respectively, to assess the interaction differences between two experiments. When multiple replicates are present in experiments, our approaches provide the flexibility for users to either pool all replicates together to therefore increase the network coverage, or to use the replicates in parallel to increase the signal to noise ratio. We show that while the local method works better in detecting changes from simulated networks, the global method performs better on real Hi-C data. The local and global methods, regardless of pooling, are always superior to two existing methods. Furthermore, our methods work well on both unweighted and weighted networks and our normalization strategy significantly improves the performance compared with raw networks without normalization. Therefore, we believe our methods will be useful for identifying differentially interacting genomic regions.

Genome-wide mapping of allele-specific protein-DNA interactions in human cells

2008 ◽  
Vol 5 (4) ◽  
pp. 307-309 ◽  
Author(s):  
Nathaniel D Maynard ◽  
Jing Chen ◽  
Rhona K Stuart ◽  
Jian-Bing Fan ◽  
Bing Ren
Keyword(s):  
Specific Protein ◽  
Human Cells ◽  
Dna Interactions ◽  
Genome Wide ◽  
Protein Dna Interactions ◽  
Allele Specific

scholarly journals High resolution discovery of chromatin interactions

2018 ◽  
Author(s):  
Yuchun Guo ◽  
Konstantin Krismer ◽  
Michael Closser ◽  
Hynek Wichterle ◽  
David K. Gifford
Keyword(s):  
De Novo ◽  
Interaction Analysis ◽  
Computational Method ◽  
Detection Methods ◽  
Chromatin Interaction ◽  
Dynamic Interactions ◽  
Interaction Detection ◽  
Chromatin Interactions ◽  
Linkage Information ◽  
Functional Consequences

ABSTRACTChromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is a method for the genome-wide de novo discovery of chromatin interactions. Existing computational methods typically fail to detect weak or dynamic interactions because they use a peak-calling step that ignores paired-end linkage information. We have developed a novel computational method called Chromatin Interaction Discovery (CID) to overcome this limitation with an unbiased clustering approach for interaction discovery. CID outperforms existing chromatin interaction detection methods with improved sensitivity, replicate consistency, and concordance with other chromatin interaction datasets. In addition, CID also outperforms other methods in discovering chromatin interactions from HiChIP data. We expect that the CID method will be valuable in characterizing 3D chromatin interactions and in understanding the functional consequences of disease-associated distal genetic variations.

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