scholarly journals Filling annotation gaps in yeast genomes using genome-wide contact maps

2014 ◽  
Vol 30 (15) ◽  
pp. 2105-2113 ◽  
Author(s):  
Hervé Marie-Nelly ◽  
Martial Marbouty ◽  
Axel Cournac ◽  
Gianni Liti ◽  
Gilles Fischer ◽  
...  
Keyword(s):  
Contact Maps ◽  
Genome Wide ◽  
Wide Contact ◽  
Yeast Genomes

scholarly journals A supervised learning framework for chromatin loop detection in genome-wide contact maps

2019 ◽  
Author(s):  
Tarik J. Salameh ◽  
Xiaotao Wang ◽  
Fan Song ◽  
Bo Zhang ◽  
Sage M. Wright ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Data Types ◽  
Random Forest Classification ◽  
Chromatin Loops ◽  
3D Genome ◽  
Contact Maps ◽  
Genome Wide ◽  
A Genome ◽  
Loop Detection ◽  
Wide Contact

ABSTRACTAccurately predicting chromatin loops from genome-wide interaction matrices such as Hi-C data is critical to deepen our understanding of proper gene regulation events. Current approaches are mainly focused on searching for statistically enriched dots on a genome-wide map. However, given the availability of a wide variety of orthogonal data types such as ChIA-PET, GAM, SPRITE, and high-throughput imaging, a supervised learning approach could facilitate the discovery of a comprehensive set of chromatin interactions. Here we present Peakachu, a Random Forest classification framework that predicts chromatin loops from genome-wide contact maps. Compared with current enrichment-based approaches, Peakachu identified more meaningful short-range interactions. We show that our models perform well in different platforms such as Hi-C, Micro-C, and DNA SPRITE, across different sequencing depths, and across different species. We applied this framework to systematically predict chromatin loops in 56 Hi-C datasets, and the results are available at the 3D Genome Browser (www.3dgenome.org).

scholarly journals A supervised learning framework for chromatin loop detection in genome-wide contact maps

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Tarik J. Salameh ◽  
Xiaotao Wang ◽  
Fan Song ◽  
Bo Zhang ◽  
Sage M. Wright ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Chromatin Loop ◽  
Learning Framework ◽  
Contact Maps ◽  
Genome Wide ◽  
Loop Detection ◽  
Wide Contact

scholarly journals Computer vision for pattern detection in chromosome contact maps

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Cyril Matthey-Doret ◽  
Lyam Baudry ◽  
Axel Breuer ◽  
Rémi Montagne ◽  
Nadège Guiglielmoni ◽  
...  
Keyword(s):  
Computer Vision ◽  
Simulated Data ◽  
Pattern Detection ◽  
Training Dataset ◽  
Biological Functions ◽  
Prior Training ◽  
Chromosome Conformation ◽  
Contact Maps ◽  
Genome Wide ◽  
Wide Contact

AbstractChromosomes of all species studied so far display a variety of higher-order organisational features, such as self-interacting domains or loops. These structures, which are often associated to biological functions, form distinct, visible patterns on genome-wide contact maps generated by chromosome conformation capture approaches such as Hi-C. Here we present Chromosight, an algorithm inspired from computer vision that can detect patterns in contact maps. Chromosight has greater sensitivity than existing methods on synthetic simulated data, while being faster and applicable to any type of genomes, including bacteria, viruses, yeasts and mammals. Our method does not require any prior training dataset and works well with default parameters on data generated with various protocols.

scholarly journals Boost-HiC: computational enhancement of long-range contacts in chromosomal contact maps

2019 ◽  
Vol 35 (16) ◽  
pp. 2724-2729 ◽  
Author(s):  
L Carron ◽  
J B Morlot ◽  
V Matthys ◽  
A Lesne ◽  
J Mozziconacci
Keyword(s):  
Long Range ◽  
Short Range ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Missing Information ◽  
High Confidence ◽  
Contact Maps ◽  
Genome Wide ◽  
Algorithmic Procedure

Abstract Motivation Genome-wide chromosomal contact maps are widely used to uncover the 3D organization of genomes. They rely on collecting millions of contacting pairs of genomic loci. Contacts at short range are usually well measured in experiments, while there is a lot of missing information about long-range contacts. Results We propose to use the sparse information contained in raw contact maps to infer high-confidence contact counts between all pairs of loci. Our algorithmic procedure, Boost-HiC, enables the detection of Hi-C patterns such as chromosomal compartments at a resolution that would be otherwise only attainable by sequencing a hundred times deeper the experimental Hi-C library. Boost-HiC can also be used to compare contact maps at an improved resolution. Availability and implementation Boost-HiC is available at https://github.com/LeopoldC/Boost-HiC. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Multiplex-GAM: genome-wide identification of chromatin contacts yields insights not captured by Hi-C

2020 ◽  
Author(s):  
Robert A. Beagrie ◽  
Christoph J. Thieme ◽  
Carlo Annunziatella ◽  
Catherine Baugher ◽  
Yingnan Zhang ◽  
...  
Keyword(s):  
Embryonic Stem ◽  
Active Regions ◽  
Detailed Comparison ◽  
Contact Mapping ◽  
3D Genome ◽  
Topologically Associating Domains ◽  
Genome Wide ◽  
Wide Contact ◽  
Genomic Regions ◽  
Mes Cells

Summary (Abstract)Technologies for measuring 3D genome topology are increasingly important for studying mechanisms of gene regulation, for genome assembly and for mapping of genome rearrangements. Hi-C and other ligation-based methods have become routine but have specific biases. Here, we develop multiplex-GAM, a faster and more affordable version of Genome Architecture Mapping (GAM), a ligation-free technique to map chromatin contacts genomewide. We perform a detailed comparison of contacts obtained by multiplex-GAM and Hi-C using mouse embryonic stem (mES) cells. We find that both methods detect similar topologically associating domains (TADs). However, when examining the strongest contacts detected by either method, we find that only one third of these are shared. The strongest contacts specifically found in GAM often involve “active” regions, including many transcribed genes and super-enhancers, whereas in Hi-C they more often contain “inactive” regions. Our work shows that active genomic regions are involved in extensive complex contacts that currently go under-estimated in genome-wide ligation-based approaches, and highlights the need for orthogonal advances in genome-wide contact mapping technologies.

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 NucleoMap: a computational tool for identifying nucleosomes in ultra-high resolution contact maps

2021 ◽  
Author(s):  
Yuanhao Huang ◽  
Bingjiang Wang ◽  
Jie Liu
Keyword(s):  
High Resolution ◽  
Spatial Organization ◽  
Embryonic Stem ◽  
Identification Methods ◽  
Contact Maps ◽  
Functional Regions ◽  
Contact Distance ◽  
Genome Wide ◽  
Capture Techniques ◽  
Nucleosome Binding

Although poorly positioned nucleosomes are ubiquitous in the prokaryote genome, they are difficult to identify with existing nucleosome identification methods. Recently available enhanced high-throughput chromatin conformation capture techniques such as Micro-C, DNase Hi-C, and Hi-CO characterize nucleosome-level chromatin proximity, probing the positions of mono-nucleosomes and the spacing between nucleosome pairs at the same time, enabling profiling of nucleosomes in poorly positioned regions. Here we develop a novel computational approach, NucleoMap, to identify nucleosome positioning from ultra-high resolution chromatin contact maps. By integrating nucleosome binding preferences, read density, and pairing information, NucleoMap precisely locates nucleosomes in both eukaryotic and prokaryotic genomes and outperforms existing nucleosome identification methods in sensitivity and specificity. We rigorously characterize genome-wide association in eukaryotes between the spatial organization of mono-nucleosomes and their corresponding histone modifications, protein binding activities, and higher-order chromatin functions. We also predict two tetra-nucleosome folding structures in human embryonic stem cells using machine learning methods and analysis their distribution at different structural and functional regions. Based on the identified nucleosomes, nucleosome contact maps are constructed, reflecting the inter-nucleosome distances and preserving the original data's contact distance profile.

scholarly journals DiffGR: Detecting differentially interacting genomic regions from Hi-C contact maps

2020 ◽  
Author(s):  
Huiling Liu ◽  
Wenxiu Ma
Keyword(s):  
Large Scale ◽  
Simulation Studies ◽  
Cell Type ◽  
Nonparametric Approach ◽  
Contact Maps ◽  
Chromatin Interactions ◽  
Topologically Associating Domain ◽  
Genome Wide ◽  
Cell Type Specific ◽  
Genomic Regions

AbstractRecent advances in Hi-C techniques have allowed us to map genome-wide chromatin interactions and uncover higher-order chromatin structures, thereby shedding light on the principles of genome architecture and functions. However, statistical methods for detecting changes in large-scale chromatin organization such as topologically-associating domain (TAD) are still lacking. We proposed a new statistical method, DiffGR, for detecting differentially interacting genomic regions at the TAD level between Hi-C contact maps. We utilized the stratum-adjusted correlation coefficient (SCC) to measure similarity of local TAD regions. We then developed a nonparametric approach to identify statistically significant changes of genomic interacting regions. Through simulation studies, we demonstrated that DiffGR can robustly and effectively discover differential genomic regions under various conditions. Furthermore, we successfully revealed cell type-specific changes in genomic interacting regions using real Hi-C datasets. DiffGR is publicly available at https://github.com/wmalab/DiffGR.

A statistical model of intra-chromosome contact maps

Soft Matter ◽  
2015 ◽  
Vol 11 (5) ◽  
pp. 1019-1025 ◽  
Author(s):  
Leonid I. Nazarov ◽  
Mikhail V. Tamm ◽  
Vladik A. Avetisov ◽  
Sergei K. Nechaev
Keyword(s):  
Fine Structure ◽  
Statistical Model ◽  
Chromosome Conformation Capture ◽  
Chromosome Conformation ◽  
Contact Maps ◽  
Genome Wide ◽  
A Genome ◽  
Capture Method ◽  
Chromosome Maps

A statistical model describing a fine structure of the intra-chromosome maps obtained by a genome-wide chromosome conformation capture method (Hi–C) is proposed.

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