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 The 3D Genome Browser: a web-based browser for visualizing 3D genome organization and long-range chromatin interactions

2017 ◽  
Author(s):  
Yanli Wang ◽  
Bo Zhang ◽  
Lijun Zhang ◽  
Lin An ◽  
Jie Xu ◽  
...  
Keyword(s):  
Genome Organization ◽  
Target Genes ◽  
Genome Structure ◽  
Regulatory Elements ◽  
Genomic Region ◽  
Genome Browser ◽  
Chromatin Interaction ◽  
Interaction Data ◽  
3D Genome ◽  
Chromatin Interactions

ABSTRACTRecent advent of 3C-based technologies such as Hi-C and ChIA-PET provides us an opportunity to explore chromatin interactions and 3D genome organization in an unprecedented scale and resolution. However, it remains a challenge to visualize chromatin interaction data due to its size and complexity. Here, we introduce the 3D Genome Browser (http://3dgenome.org), which allows users to conveniently explore both publicly available and their own chromatin interaction data. Users can also seamlessly integrate other “omics” data sets, such as ChIP-Seq and RNA-Seq for the same genomic region, to gain a complete view of both regulatory landscape and 3D genome structure for any given gene. Finally, our browser provides multiple methods to link distal cis-regulatory elements with their potential target genes, including virtual 4C, ChIA-PET, Capture Hi-C and cross-cell-type correlation of proximal and distal DNA hypersensitive sites, and therefore represents a valuable resource for the study of gene regulation in mammalian genomes.

scholarly journals Accurate loop calling for 3D genomic data with cLoops

2018 ◽  
Author(s):  
Yaqiang Cao ◽  
Xingwei Chen ◽  
Daosheng Ai ◽  
Zhaoxiong Chen ◽  
Guoyu Chen ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Genome Mapping ◽  
Statistical Significance ◽  
Regulatory Elements ◽  
Data Type ◽  
Sequencing Data ◽  
3D Genome ◽  
Local Background ◽  
Single Data ◽  
Hardware Costs

AbstractSequencing-based 3D genome mapping technologies can identify loops formed by interactions between regulatory elements hundreds of kilobases apart. Existing loop-calling tools are mostly restricted to a single data type, with accuracy dependent on a pre-defined resolution contact matrix or called peaks, and can have prohibitive hardware costs. Here we introduce cLoops (‘see loops’) to address these limitations. cLoops is based on the clustering algorithm cDBSCAN that directly analyzes the paired-end tags (PETs) to find candidate loops and uses a permuted local background to estimate statistical significance. These two data-type-independent processes enable loops to be reliably identified for both sharp and broad peak data, including but not limited to ChIA-PET, Hi-C, HiChIP and Trac-looping data. Loops identified by cLoops showed much less distance-dependent bias and higher enrichment relative to local regions than existing tools. Altogether, cLoops improves accuracy of detecting of 3D-genomic loops from sequencing data, is versatile, flexible, efficient, and has modest hardware requirements, and is freely available at: https://github.com/YaqiangCao/cLoops.

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 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 Accurate loop calling for 3D genomic data with cLoops

2019 ◽  
Author(s):  
Yaqiang Cao ◽  
Zhaoxiong Chen ◽  
Xingwei Chen ◽  
Daosheng Ai ◽  
Guoyu Chen ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Genome Mapping ◽  
Statistical Significance ◽  
Regulatory Elements ◽  
Data Type ◽  
Supplementary Information ◽  
Sequencing Data ◽  
3D Genome ◽  
Local Background ◽  
Hardware Costs

Abstract Motivation Sequencing-based 3D genome mapping technologies can identify loops formed by interactions between regulatory elements hundreds of kilobases apart. Existing loop-calling tools are mostly restricted to a single data type, with accuracy dependent on a predefined resolution contact matrix or called peaks, and can have prohibitive hardware costs. Results Here, we introduce cLoops (‘see loops’) to address these limitations. cLoops is based on the clustering algorithm cDBSCAN that directly analyzes the paired-end tags (PETs) to find candidate loops and uses a permuted local background to estimate statistical significance. These two data-type-independent processes enable loops to be reliably identified for both sharp and broad peak data, including but not limited to ChIA-PET, Hi-C, HiChIP and Trac-looping data. Loops identified by cLoops showed much less distance-dependent bias and higher enrichment relative to local regions than existing tools. Altogether, cLoops improves accuracy of detecting of 3D-genomic loops from sequencing data, is versatile, flexible, efficient, and has modest hardware requirements. Availability and implementation cLoops with documentation and example data are freely available at: https://github.com/YaqiangCao/cLoops. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Genome assembly of the maize inbred line A188 provides a new reference genome for functional genomics

2021 ◽  
Author(s):  
Fei Ge ◽  
Jingtao Qu ◽  
Peng Liu ◽  
Lang Pan ◽  
Chaoying Zou ◽  
...  
Keyword(s):  
Single Molecule ◽  
Inbred Line ◽  
Genome Mapping ◽  
Maize Inbred Line ◽  
Sequencing Data ◽  
Structural Variations ◽  
Single Molecule Sequencing ◽  
Maize Genetic ◽  
Induction Ratio ◽  
Phenotypic Variations

Heretofore, little is known about the mechanism underlying the genotype-dependence of embryonic callus (EC) induction, which has severely inhibited the development of maize genetic engineering. Here, we report the genome sequence and annotation of a maize inbred line with high EC induction ratio, A188, which is assembled from single-molecule sequencing and optical genome mapping. We assembled a 2,210 Mb genome with a scaffold N50 size of 11.61 million bases (Mb), compared to those of 9.73 Mb for B73 and 10.2 Mb for Mo17. Comparative analysis revealed that ~30% of the predicted A188 genes had large structural variations to B73, Mo17 and W22 genomes, which caused considerable protein divergence and might lead to phenotypic variations between the four inbred lines. Combining our new A188 genome, previously reported QTLs and RNA sequencing data, we reveal 8 large structural variation genes and 4 differentially expressed genes playing potential roles in EC induction.

scholarly journals Instrumentation des activités des tuteurs à l’aide d’un système multi-agents d’analyse automatique des interactions

2011 ◽  
Vol Volume 14 - 2011 - Special... ◽  
Author(s):  
Ilham Oumaira ◽  
Rochdi Messoussi ◽  
Raja TOUAHNI
Keyword(s):  
Nous Avons ◽  
Social Cognitive ◽  
Adaptive System ◽  
Interaction Analysis ◽  
Interaction Data ◽  
Multi Agent System ◽  
Système Adaptatif ◽  
International Audience ◽  
Multi Agent ◽  
Multi Agents

International audience Research presented in this article is dedicated to the tutor instrumentation in distance collaborative learning situations. We are particularly interested in the reuse of interaction analysis indicators. In this paper, we present our system SYSAT; a multi-agent system for monitoring the activities of learners. The aim of SYSAT is to reuse indicators (social, cognitive, emotional ...) reported in the literature, in an open and adaptive system. We tested our system on the interaction data from two experiments conducted with two master students of the Ibn Tofail University. The article presents the results and discusses the prospects for Research. Ce travail s'inscrit dans le cadre des recherches sur les Environnements Informatiques pour l'Apprentissage Humain (EIAH), et plus particulièrement dans l’assistance du tuteur dans le suivi des apprenants lors des activités d’apprentissage collaboratives en ligne. Cet article décrit l’architecture du système SYSAT, un système multi-agents d’analyse automatique des interactions. L’objectif de SYSAT est de réutiliser les indicateurs (sociaux, cognitifs, affectifs…) rapportés dans la littérature, au sein d’un système adaptatif et ouvert. Nous avons testé notre système sur les données d’interactions issues de deux expérimentations menées avec les étudiants de deux masters à l’université Ibn Tofail. L’article présente les résultats obtenus et évoque les perspectives de recherche.

Sign in / Sign up

Export Citation Format

Share Document