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.

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 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 A hybrid de novo genome assembly of the honeybee, Apis mellifera, with chromosome-length scaffolds

2018 ◽  
Author(s):  
Andreas Wallberg ◽  
Ignas Bunikis ◽  
Olga Vinnere Pettersson ◽  
Mai-Britt Mosbech ◽  
Anna K. Childers ◽  
...  
Keyword(s):  
Apis Mellifera ◽  
Genome Assembly ◽  
De Novo ◽  
Hybrid Approach ◽  
Chromosome Length ◽  
Chromatin Interaction ◽  
De Novo Genome Assembly ◽  
Functional Genetic Variation ◽  
Linkage Information ◽  
Interaction Map

AbstractBackgroundThe ability to generate long sequencing reads and access long-range linkage information is revolutionizing the quality and completeness of genome assemblies. Here we use a hybrid approach that combines data from four genome sequencing and mapping technologies to generate a new genome assembly of the honeybee Apis mellifera. We first generated contigs based on PacBio sequencing libraries, which were then merged with linked-read 10x Chromium data followed by scaffolding using a BioNano optical genome map and a Hi-C chromatin interaction map, complemented by a genetic linkage map.ResultsEach of the assembly steps reduced the number of gaps and incorporated a substantial amount of additional sequence into scaffolds. The new assembly (Amel_HAv3) is significantly more contiguous and complete than the previous one (Amel_4.5), based mainly on Sanger sequencing reads. N50 of contigs is 120-fold higher (5.381 Mbp compared to 0.053 Mbp) and we anchor >98% of the sequence to chromosomes. All of the 16 chromosomes are represented as single scaffolds with an average of three sequence gaps per chromosome. The improvements are largely due to the inclusion of repetitive sequence that was unplaced in previous assemblies. In particular, our assembly is highly contiguous across centromeres and telomeres and includes hundreds of AvaI and AluI repeats associated with these features.ConclusionsThe improved assembly will be of utility for refining gene models, studying genome function, mapping functional genetic variation, identification of structural variants, and comparative genomics.

scholarly journals Probing multi-way chromatin interaction with hypergraph representation learning

2020 ◽  
Author(s):  
Ruochi Zhang ◽  
Jian Ma
Keyword(s):  
Genome Organization ◽  
Representation Learning ◽  
Higher Order ◽  
Computational Method ◽  
Chromatin Interaction ◽  
Interaction Data ◽  
3D Genome ◽  
Chromatin Interactions ◽  
Single Nucleus ◽  
And Function

AbstractAdvances in high-throughput mapping of 3D genome organization have enabled genome-wide characterization of chromatin interactions. However, proximity ligation based mapping approaches for pairwise chromatin interaction such as Hi-C cannot capture multi-way interactions, which are informative to delineate higher-order genome organization and gene regulation mechanisms at single-nucleus resolution. The very recent development of ligation-free chromatin interaction mapping methods such as SPRITE and ChIA-Drop has offered new opportunities to uncover simultaneous interactions involving multiple genomic loci within the same nuclei. Unfortunately, methods for analyzing multi-way chromatin interaction data are significantly underexplored. Here we develop a new computational method, called MATCHA, based on hypergraph representation learning where multi-way chromatin interactions are represented as hyperedges. Applications to SPRITE and ChIA-Drop data suggest that MATCHA is effective to denoise the data and make de novo predictions of multi-way chromatin interactions, reducing the potential false positives and false negatives from the original data. We also show that MATCHA is able to distinguish between multi-way interaction in a single nucleus and combination of pairwise interactions in a cell population. In addition, the embeddings from MATCHA reflect 3D genome spatial localization and function. MATCHA provides a promising framework to significantly improve the analysis of multi-way chromatin interaction data and has the potential to offer unique insights into higher-order chromosome organization and function.

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 Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Fan Cao ◽  
Yu Zhang ◽  
Yichao Cai ◽  
Sambhavi Animesh ◽  
Ying Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Rna Polymerase Ii ◽  
Dna Sequences ◽  
Lymphocytic Leukemia ◽  
Computational Method ◽  
Chromatin Interaction ◽  
Open Chromatin ◽  
Interaction Prediction ◽  
Chromatin Interactions ◽  
Genome Wide

AbstractChromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences. ChINN predicts CTCF- and RNA polymerase II-associated and Hi-C chromatin interactions. ChINN shows good across-sample performances and captures various sequence features for chromatin interaction prediction. We apply ChINN to 6 chronic lymphocytic leukemia (CLL) patient samples and a published cohort of 84 CLL open chromatin samples. Our results demonstrate extensive heterogeneity in chromatin interactions among CLL patient samples.

scholarly journals Predicting chromatin interactions between open chromatin regions from DNA sequences

2019 ◽  
Author(s):  
Fan Cao ◽  
Ying Zhang ◽  
Yan Ping Loh ◽  
Yichao Cai ◽  
Melissa J. Fullwood
Keyword(s):  
Rna Polymerase Ii ◽  
Dna Sequences ◽  
State Of The Art ◽  
Lymphocytic Leukemia ◽  
Computational Method ◽  
Chromatin Interaction ◽  
Open Chromatin ◽  
Rna Seq ◽  
Chromatin Interactions ◽  
Genome Wide

AbstractChromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is very limited. Various computational methods have been developed to predict chromatin interactions. Most of these methods rely on large collections of ChIP-Seq/RNA-Seq/DNase-Seq datasets and predict only enhancer-promoter interactions. Some of the ‘state-of-the-art’ methods have poor experimental designs, leading to over-exaggerated performances and misleading conclusions. Here we developed a computational method, Chromatin Interaction Neural Network (CHINN), to predict chromatin interactions between open chromatin regions by using only DNA sequences of the interacting open chromatin regions. CHINN is able to predict CTCF- and RNA polymerase II-associated chromatin interactions between open chromatin regions. CHINN also shows good across-sample performances and captures various sequence features that are predictive of chromatin interactions. We applied CHINN to 84 chronic lymphocytic leukemia (CLL) samples and detected systematic differences in the chromatin interactome between IGVH-mutated and IGVH-unmutated CLL samples.

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 HiCcompare: a method for joint normalization of Hi-C datasets and differential chromatin interaction detection

2017 ◽  
Author(s):  
John C. Stansfield ◽  
Mikhail G. Dozmorov
Keyword(s):  
Gene Expression ◽  
Comparative Analysis ◽  
Regulation Of Gene Expression ◽  
3D Structure ◽  
Real Life ◽  
R Package ◽  
Chromatin Interaction ◽  
Interaction Detection ◽  
Chromatin Interactions ◽  
A Genome

AbstractChanges in spatial chromatin interactions are now emerging as a unifying mechanism or-chestrating regulation of gene expression. Evolution of chromatin conformation capture methods into Hi-C sequencing technology now allows an insight into chromatin interactions on a genome-wide scale. However, Hi-C data contains many DNA sequence- and technology-driven biases. These biases prevent effective comparison of chromatin interactions aimed at identifying genomic regions differentially interacting between, disease-normal states or different cell types. Several methods have been developed for normalizing individual Hi-C datasets. However, they fail to account for biases between two or more Hi-C datasets, hindering comparative analysis of chromatin interactions. We developed a simple and effective method HiCcompare for the joint normalization and differential analysis of multiple Hi-C datasets. The method avoids constraining Hi-C data within a rigid statistical model, allowing a data-driven normalization of biases using locally weighted linear regression (loess). The method identifies region-specific chromatin interaction changes complementary to changes due to large-scale genomic rearrangements, such as copy number variants (CNVs). HiCcompare outperforms methods for normalizing individual Hi-C datasets in detecting a priori known chromatin interaction differences in simulated and real-life settings while detecting biologically relevant changes. HiCcompare is freely available as a Bioconductor R package https://bioconductor.org/packages/HiCcompare/.Author SummaryAdvances in chromosome conformation capture sequencing technologies (Hi-C) have sparked interest in studying the 3-dimensional (3D) chromatin interaction structure of the human genome. The 3D structure of the genome is now considered as a primary regulator of gene expression. Changes to the 3D chromatin interactions are now emerging as a hallmark of cancer and other complex diseases. With the growing availability of Hi-C data generated under different conditions (e.g. tumor-normal, cell-type-specific), methods are needed to compare them. However, biases in Hi-C data hinder their comparative analysis. To account for biases, several normalization techniques have been developed for removing biases in individual Hi-C datasets, but very few were designed to account for between-datasets biases. We developed a new method and R package HiCcompare for the joint normalization of multiple Hi-C datasets and differential chromatin interaction detection. Our results show the superiority of our joint normalization methods compared to methods for normalizing individual datasets in detecting true chromatin interaction changes. HiCcompare enables further research into discovering the dynamics of 3D genomic changes.

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