scholarly journals Chromatin Immunoprecipitation for Chromatin Interaction Analysis Using Paired-End-Tag (ChIA-PET) Sequencing in Tadpole Tissues

2018 ◽  
Vol 2018 (8) ◽  
pp. pdb.prot097725 ◽  
Author(s):  
Nicolas Buisine ◽  
Xiaoan Ruan ◽  
Yijun Ruan ◽  
Laurent M. Sachs
Keyword(s):  
Chromatin Immunoprecipitation ◽  
Interaction Analysis ◽  
Chromatin Interaction

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 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 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 Chromatin 3D interaction analysis of the STARD10 locus unveils FCHSD2 as a new regulator of insulin secretion

2020 ◽  
Author(s):  
Ming Hu ◽  
Inês Cebola ◽  
Gaelle Carrat ◽  
Shuying Jiang ◽  
Sameena Nawaz ◽  
...  
Keyword(s):  
Insulin Secretion ◽  
Pancreatic Beta Cells ◽  
Cell Function ◽  
Interaction Analysis ◽  
Association Studies ◽  
Ctcf Binding ◽  
Chromatin Interaction ◽  
Chromatin Domain ◽  
Genome Wide Association Studies ◽  
3D Interaction

SUMMARYGenome-wide association studies have identified thousands of genetic variants associated with type 2 diabetes (T2D) risk. Using chromatin conformation capture we show that an enhancer cluster in the STARD10 T2D locus forms a defined 3D chromatin domain. A 4.1 Kb region within this region, carrying five disease-associated variants, physically interacts with CTCF-binding regions and with an enhancer possessing strong transcriptional activity. Analysis of human islet 3D chromatin interaction maps identifies FCHSD2 gene as an additional target of the enhancer cluster. CRISPR-Cas9-mediated deletion of the variant region, or of an associated enhancer, in human pancreatic beta cells impaired glucose-stimulated insulin secretion. Expression of both STARD10 and FCHSD2, but not ARAP1, was reduced in cells harboring CRISPR deletions, and expression of STARD10 and FCHSD2 was associated with the possession of variant alleles in human islets. Finally, CRISPR-Cas9-mediated loss of STARD10 or FCHSD2 impaired regulated insulin secretion. Thus, multiple genes at the STARD10 locus influence β cell function.

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 Characteristic arrangement of nucleosomes is predictive of chromatin interactions at kilobase resolution

2016 ◽  
Author(s):  
Hui Zhang ◽  
Feifei Li ◽  
Yan Jia ◽  
Bingxiang Xu ◽  
Yiqun Zhang ◽  
...  
Keyword(s):  
Interaction Analysis ◽  
Low Cost ◽  
Genome Structure ◽  
Cell Types ◽  
Regulatory Elements ◽  
Chromosome Conformation Capture ◽  
Computational Method ◽  
Chromatin Interaction ◽  
Chromosome Conformation ◽  
Interaction Sites

AbstractHigh-throughput chromosome conformation capture technologies, such as Hi-C, have made it possible to survey 3D genome structure. However, the ability to obtain 3D profiles at kilobase resolution at low cost remains a major challenge. Therefore, we herein report a computational method to precisely identify chromatin interaction sites at kilobase resolution from MNase-seq data, termed chromatin interaction site detector (CISD), and a CISD-based chromatin loop predictor (CISD_loop) that predicts chromatin-chromatin interaction (CCI) from low-resolution Hi-C data. The methods are built on a hypothesis that CCIs result in a characteristic nucleosome arrangement pattern flanking the interaction sites. Accordingly, we show that the predictions of CISD and CISD_loop overlap closely with chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) anchors and loops, respectively. Moreover, the methods trained in one cell type can be applied to other cell types with high accuracy. The validity of the methods was further supported by chromosome conformation capture (3C) experiments at 5kb resolution. Finally, we demonstrate that only modest amounts of MNase-seq and Hi-C data are sufficient to achieve ultrahigh resolution CCI map. The predictive power of CISD/CISD_loop supports the hypothesis that CCIs induce local nucleosome rearrangement and that the pattern may serve as probes for 3D dynamics of the genome. Thus, our method will facilitate precise and systematic investigations of the interactions between distal regulatory elements on a larger scale than hitherto have been possible.

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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