scholarly journals Nucleosome positions alone determine micro-domains in yeast chromosomes

2018 ◽  
Author(s):  
O. Wiese ◽  
D. Marenduzzo ◽  
C. A. Brackley
Keyword(s):  
Length Distribution ◽  
Yeast Genome ◽  
Chromatin Interactions ◽  
Genome Wide ◽  
Simple Simulation ◽  
Domain Boundaries ◽  
Dynamics Simulations ◽  
Genomic Regions ◽  
Inactive Genes ◽  
High Degree

AbstractWe use molecular dynamics simulations based on publicly available MNase-seq data for nucleosome positions to predict the 3-D structure of chromatin in the yeast genome. Our main aim is to shed light on the mechanism underlying the formation of micro-domains, chromosome regions of around 0.5-10 kbp which show enriched self-interactions, which were experimentally observed in recent MicroC experiments. We show that the sole input of nucleosome positioning data is already sufficient to determine the patterns of chromatin interactions and domain boundaries seen experimentally to a high degree of accuracy. Since the nucleosome spacing so strongly affects the larger-scale domain structure, we next examine the genome-wide linker-length distribution in more detail, finding that it is highly irregular, and varies in different genomic regions such as gene bodies, promoters, and active and inactive genes. Finally we use our simple simulation model to characterise in more detail how irregular nucleosome spacing may affect local chromatin structure.

scholarly journals Nucleosome positions alone can be used to predict domains in yeast chromosomes

2019 ◽  
Vol 116 (35) ◽  
pp. 17307-17315 ◽  
Author(s):  
Oliver Wiese ◽  
Davide Marenduzzo ◽  
Chris A. Brackley
Keyword(s):  
3D Structure ◽  
Length Distribution ◽  
Micrococcal Nuclease ◽  
Yeast Genome ◽  
Sequencing Data ◽  
Simple Simulation ◽  
Interaction Domains ◽  
Dynamics Simulations ◽  
Genomic Regions ◽  
High Degree

We use molecular dynamics simulations based on publicly available micrococcal nuclease sequencing data for nucleosome positions to predict the 3D structure of chromatin in the yeast genome. Our main aim is to shed light on the mechanism underlying the formation of chromosomal interaction domains, chromosome regions of around 0.5 to 10 kbp which show enriched self-interactions, which were experimentally observed in recent MicroC experiments (importantly these are at a different length scale from the 100- to 1,000-kbp–sized domains observed in higher eukaryotes). We show that the sole input of nucleosome positioning data is already sufficient to determine the patterns of chromatin interactions and domain boundaries seen experimentally to a high degree of accuracy. Since the nucleosome spacing so strongly affects the larger-scale domain structure, we next examine the genome-wide linker-length distribution in more detail, finding that it is highly irregular and varies in different genomic regions such as gene bodies, promoters, and active and inactive genes. Finally we use our simple simulation model to characterize in more detail how irregular nucleosome spacing may affect local chromatin structure.

scholarly journals GAMtools: an automated pipeline for analysis of Genome Architecture Mapping data

2017 ◽  
Author(s):  
Robert A. Beagrie ◽  
Markus Schueler
Keyword(s):  
Next Generation Sequencing Data ◽  
Genome Architecture ◽  
Sequencing Data ◽  
Cell Nuclei ◽  
Automated Mapping ◽  
Chromatin Interactions ◽  
Genome Wide ◽  
Quality Control Metrics ◽  
Genomic Regions ◽  
Analytical Tools

AbstractGenome Architecture Mapping (GAM) is a recently developed method for mapping chromatin interactions genome-wide. GAM is based on sequencing genomic DNA extracted from thin cryosections of cell nuclei. As a new approach, GAM datasets require specialized analytical tools and approaches. Here we present GAMtools, a pipeline for analysing GAM datasets. GAMtools covers the automated mapping of raw next-generation sequencing data generated by GAM, detection of genomic regions present in each nuclear slice, calculation of quality control metrics, generation of inferred proximity matrices, plotting of heatmaps and detection of genomic features for which chromatin interactions are enriched/depleted.

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.

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.

scholarly journals Detecting local changes in chromatin architecture with false discovery control

2020 ◽  
Author(s):  
Hillary Koch ◽  
Tao Yang ◽  
Maxim Imakaev ◽  
Ross C. Hardison ◽  
Qunhua Li
Keyword(s):  
Spatial Modeling ◽  
Strong Correlations ◽  
Chromatin Architecture ◽  
3D Space ◽  
Chromatin Interactions ◽  
False Discovery ◽  
Genome Wide ◽  
Powerful Means ◽  
Genomic Regions ◽  
False Discoveries

AbstractHi-C experiments are a powerful means to describe the organization of chromatin interactions genome-wide. By using Hi-C data to identify differentially organized genomic regions, relationships between this organization, gene expression, and cell identity may be established. However, Hi-C data exhibit a unique and challenging spatial structure, as genomic loci can show strong correlations when they are nearby in 3D space within the nucleus or 1D space along the chromosome. Consequently, the development of methods that can accurately detect differences between Hi-C samples while controlling false discoveries has remained difficult. To meet this need, we introduce a spatial modeling approach based on sliding window statistics. Using polymer simulations, we illustrate the improved power and precision of our method to identify differentially interacting genomic regions. We further demonstrate our method’s ability to reveal biologically meaningful changes in chromatin architecture through two data analyses concerning the loss of architectural and chromatin remodeling proteins.

scholarly journals High resolution single-cell chromatin 3D modeling reveals coherent chromatin aggregation with varied structures in controlling genome function stability

2019 ◽  
Author(s):  
Luming Meng ◽  
Yi Shi ◽  
Chenxi Wang
Keyword(s):  
Single Cell ◽  
Gene Activation ◽  
Strongly Correlated ◽  
Chromosome Conformation ◽  
3D Genome ◽  
Chromatin Interactions ◽  
Genome Wide ◽  
Consistent Aggregation ◽  
3D Architecture ◽  
Genomic Regions

The genome 3D architecture is thought to be related to regulating gene expression levels in cells and can be explained by genome-wide chromatin interactions which have been explored by chromosome conformation capture based techniques, especially Hi-C. Based on single-cell Hi-C data, we developed a new method in constructing experimental consistent 3D intact genome structures for individual cells with a resolution of 10kb or higher. The modeled structures showed marked variations of 3D genome organization across different cells. However, chromosome loci marked by different proteins, such as CTCF and post-translationally modified histones, are consistently non-specifically aggregated in space. Interestingly, similar aggregations between active enhancers and active promoters were observed, especially for those separated by genomic regions of the scale of megabase or larger. Such long-range associations between active enhancers and promoters are strongly correlated with spatial aggregation of chromosome loci marked by different proteins. Through analyzing the 3D structures of intact genome, we proposed that coherent gene activation profiles among individual cells can be achieved by the consistent aggregation of protein marked loci instead of maintaining identical folded conformations.

scholarly journals Genome-wide association study in almost 195,000 individuals identifies 50 previously unidentified genetic loci for eye color

2021 ◽  
Vol 7 (11) ◽  
pp. eabd1239
Author(s):  
Mark Simcoe ◽  
Ana Valdes ◽  
Fan Liu ◽  
Nicholas A. Furlotte ◽  
David M. Evans ◽  
...  
Keyword(s):  
Association Study ◽  
Genome Wide Association Study ◽  
Color Variation ◽  
Genome Wide Association ◽  
Nucleotide Polymorphisms ◽  
Melanin Pigmentation ◽  
Eye Color ◽  
Human Eye ◽  
Genome Wide ◽  
Genomic Regions

Human eye color is highly heritable, but its genetic architecture is not yet fully understood. We report the results of the largest genome-wide association study for eye color to date, involving up to 192,986 European participants from 10 populations. We identify 124 independent associations arising from 61 discrete genomic regions, including 50 previously unidentified. We find evidence for genes involved in melanin pigmentation, but we also find associations with genes involved in iris morphology and structure. Further analyses in 1636 Asian participants from two populations suggest that iris pigmentation variation in Asians is genetically similar to Europeans, albeit with smaller effect sizes. Our findings collectively explain 53.2% (95% confidence interval, 45.4 to 61.0%) of eye color variation using common single-nucleotide polymorphisms. Overall, our study outcomes demonstrate that the genetic complexity of human eye color considerably exceeds previous knowledge and expectations, highlighting eye color as a genetically highly complex human trait.

scholarly journals A deformation energy model reveals sequence-dependent property of nucleosome positioning

Chromosoma ◽  
2021 ◽  
Vol 130 (1) ◽  
pp. 27-40
Author(s):  
Guoqing Liu ◽  
Hongyu Zhao ◽  
Hu Meng ◽  
Yongqiang Xing ◽  
Lu Cai
Keyword(s):  
Budding Yeast ◽  
Nucleosome Occupancy ◽  
Nucleosome Positioning ◽  
Deformation Energy ◽  
Energy Model ◽  
Structural Parameter ◽  
High Deformation ◽  
Dna Deformation ◽  
Genome Wide ◽  
Genomic Regions

AbstractWe present a deformation energy model for predicting nucleosome positioning, in which a position-dependent structural parameter set derived from crystal structures of nucleosomes was used to calculate the DNA deformation energy. The model is successful in predicting nucleosome occupancy genome-wide in budding yeast, nucleosome free energy, and rotational positioning of nucleosomes. Our model also indicates that the genomic regions underlying the MNase-sensitive nucleosomes in budding yeast have high deformation energy and, consequently, low nucleosome-forming ability, while the MNase-sensitive non-histone particles are characterized by much lower DNA deformation energy and high nucleosome preference. In addition, we also revealed that remodelers, SNF2 and RSC8, are likely to act in chromatin remodeling by binding to broad nucleosome-depleted regions that are intrinsically favorable for nucleosome positioning. Our data support the important role of position-dependent physical properties of DNA in nucleosome positioning.

scholarly journals Epigenome-Wide Study Identified Methylation Sites Associated with the Risk of Obesity

Nutrients ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1984
Author(s):  
Majid Nikpay ◽  
Sepehr Ravati ◽  
Robert Dent ◽  
Ruth McPherson
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Rare Variants ◽  
Genome Wide ◽  
A Genome ◽  
Genome Wide Search ◽  
Risk Snps ◽  
Trait Locus ◽  
Genomic Regions ◽  
Eqtl Data

Here, we performed a genome-wide search for methylation sites that contribute to the risk of obesity. We integrated methylation quantitative trait locus (mQTL) data with BMI GWAS information through a SNP-based multiomics approach to identify genomic regions where mQTLs for a methylation site co-localize with obesity risk SNPs. We then tested whether the identified site contributed to BMI through Mendelian randomization. We identified multiple methylation sites causally contributing to the risk of obesity. We validated these findings through a replication stage. By integrating expression quantitative trait locus (eQTL) data, we noted that lower methylation at cg21178254 site upstream of CCNL1 contributes to obesity by increasing the expression of this gene. Higher methylation at cg02814054 increases the risk of obesity by lowering the expression of MAST3, whereas lower methylation at cg06028605 contributes to obesity by decreasing the expression of SLC5A11. Finally, we noted that rare variants within 2p23.3 impact obesity by making the cg01884057 site more susceptible to methylation, which consequently lowers the expression of POMC, ADCY3 and DNAJC27. In this study, we identify methylation sites associated with the risk of obesity and reveal the mechanism whereby a number of these sites exert their effects. This study provides a framework to perform an omics-wide association study for a phenotype and to understand the mechanism whereby a rare variant causes a disease.

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