scholarly journals Inferring Chromosome Radial Organization from Hi-C Data

2019 ◽  
Author(s):  
Priyojit Das ◽  
Tongye Shen ◽  
Rachel Patton McCord
Keyword(s):  
Genome Structure ◽  
3D Structure ◽  
Cell Types ◽  
Directed Network ◽  
Chromosome Territories ◽  
Imaging Data ◽  
Microscopy Imaging ◽  
Chromosome Conformation ◽  
Eukaryotic Chromosome ◽  
Radial Organization

AbstractBackgroundThe nonrandom radial organization of eukaryotic chromosome territories (CTs) inside the nucleus plays an important role in nuclear functional compartmentalization. Increasingly, chromosome conformation capture (Hi-C) based approaches are being used to characterize the genome structure of many cell types and conditions. Computational methods to extract 3D arrangements of CTs from this type of pairwise contact data will thus increase our ability to analyze CT organization in a wider variety of biological situations.ResultsA number of full-scale polymer models have successfully reconstructed the 3D structure of chromosome territories from Hi-C. To supplement such methods, we explore alternative, direct, and less computationally intensive approaches to capture radial CT organization from Hi-C data. We show that we can infer relative chromo-some ordering using PCA on a thresholded inter-chromosomal contact matrix. We simulate an ensemble of possible CT arrangements using a force-directed network layout algorithm and propose an approach to integrate additional chromosome properties into our predictions. Our CT radial organization predictions have a high correlation with microscopy imaging data for various cell nucleus geometries (lymphoblastoid, skin fibroblast, and breast epithelial cells), and we can capture previously documented changes in senescent and progeria cells.ConclusionsOur analysis approaches provide rapid and modular approaches to screen for alterations in CT organization across widely available Hi-C data. We demon-strate which stages of the approach can extract meaningful information, and also de-scribe limitations of pairwise contacts alone to predict absolute 3D positions.

scholarly journals Inferring chromosome radial organization from Hi-C data

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Priyojit Das ◽  
Tongye Shen ◽  
Rachel Patton McCord
Keyword(s):  
Genome Structure ◽  
3D Structure ◽  
Cell Types ◽  
Directed Network ◽  
Chromosome Territories ◽  
Imaging Data ◽  
Microscopy Imaging ◽  
Chromosome Conformation ◽  
Eukaryotic Chromosome ◽  
Radial Organization

Abstract Background The nonrandom radial organization of eukaryotic chromosome territories (CTs) inside the nucleus plays an important role in nuclear functional compartmentalization. Increasingly, chromosome conformation capture (Hi-C) based approaches are being used to characterize the genome structure of many cell types and conditions. Computational methods to extract 3D arrangements of CTs from this type of pairwise contact data will thus increase our ability to analyze CT organization in a wider variety of biological situations. Results A number of full-scale polymer models have successfully reconstructed the 3D structure of chromosome territories from Hi-C. To supplement such methods, we explore alternative, direct, and less computationally intensive approaches to capture radial CT organization from Hi-C data. We show that we can infer relative chromosome ordering using PCA on a thresholded inter-chromosomal contact matrix. We simulate an ensemble of possible CT arrangements using a force-directed network layout algorithm and propose an approach to integrate additional chromosome properties into our predictions. Our CT radial organization predictions have a high correlation with microscopy imaging data for various cell nucleus geometries (lymphoblastoid, skin fibroblast, and breast epithelial cells), and we can capture previously documented changes in senescent and progeria cells. Conclusions Our analysis approaches provide rapid and modular approaches to screen for alterations in CT organization across widely available Hi-C data. We demonstrate which stages of the approach can extract meaningful information, and also describe limitations of pairwise contacts alone to predict absolute 3D positions.

3D genome structure reconstruction from chromosomal contact data

2017 ◽  
Author(s):  
◽  
Tuan Anh Trieu
Keyword(s):  
High Resolution ◽  
Genome Structure ◽  
Cell Types ◽  
3D Models ◽  
Graphic User Interface ◽  
Soft Constraints ◽  
Chromosome Conformation ◽  
3D Genome ◽  
Manual Adjustment ◽  
The Relationship

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Different cell types of an organism have the same DNA sequence, but they can function differently because their difference in 3D organization allows them to express different genes and has different cellular functions. Understanding the 3D organization of the genome is the key to understand functions of the cell. Chromosome conformation capture techniques like Hi-C and TCC that can capture interactions between proximal chromosome fragments have allowed the study of 3D genome organization in high resolution and high through-put. My work focuses on developing computational methods to reconstruct 3D genome structures from Hi-C data. I presented three methods to reconstruct 3D genome and chromosome structures. The first method can build 3D genome models from soft constraints of contacts and non-contacts. This method utilizes the concept of contact and non-contact to reconstruct 3D models without translating interaction frequencies into physical distances. The translation is commonly used by other methods even though it makes a strong assumption about the relationship between interaction frequencies and physical distances. In synthetic dataset, when the relationship was known, my method performed comparably with other methods assuming the relationship. This shows the potential of my method for real Hi-C datasets where the relationship is unknown. The limitation of the method is that it has parameters requiring manual adjustment. I developed the second method to reconstruct 3D genome models. This method utilizes a commonly used function to translate interaction frequencies to physical distances to build 3D models. I proposed a novel way to derive soft constraints to handle inconsistency in the data and to make the method robust. Building 3D models at high resolution is a more challenging problem as the number of constraints is small and the feasible space is larger. I introduced a third method to build 3D chromosome models at high resolution. The method reconstructs models at low resolution and then uses them to guide the reconstruction of models at high resolution. The last part of my work is the development of a comprehensive tool with intuitive graphic user interface to analyze Hi-C data, reconstruct and analyze 3D models.

scholarly journals GSDB: a database of 3D chromosome and genome structures reconstructed from Hi-C data

2019 ◽  
Author(s):  
Oluwatosin Oluwadare ◽  
Max Highsmith ◽  
Jianlin Cheng
Keyword(s):  
Structure Prediction ◽  
Biological Activities ◽  
Genome Structure ◽  
3D Structure ◽  
Three Dimensional ◽  
Chromosome Conformation ◽  
3D Genome ◽  
Reconstruction Methods ◽  
A Genome ◽  
Structure Reconstruction

ABSTRACTAdvances in the study of chromosome conformation capture (3C) technologies, such as Hi-C technique - capable of capturing chromosomal interactions in a genome-wide scale - have led to the development of three-dimensional (3D) chromosome and genome structure reconstruction methods from Hi-C data. The 3D genome structure is important because it plays a role in a variety of important biological activities such as DNA replication, gene regulation, genome interaction, and gene expression. In recent years, numerous Hi-C datasets have been generated, and likewise, a number of genome structure construction algorithms have been developed. However, until now, there has been no freely available repository for 3D chromosome structures. In this work, we outline the construction of a novel Genome Structure Database (GSDB) to create a comprehensive repository that contains 3D structures for Hi-C datasets constructed by a variety of 3D structure reconstruction tools. GSDB contains over 50,000 structures constructed by 12 state-of-the-art chromosome and genome structure prediction methods for publicly used Hi-C datasets with varying resolution. The database is useful for the community to study the function of genome from a 3D perspective. GSDB is accessible at http://sysbio.rnet.missouri.edu/3dgenome/GSDB

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 3D structure reconstruction with consideration of adjacency relationship among genomic loci

2019 ◽  
Author(s):  
Fang-Zhen Li ◽  
Zhi-E Liu ◽  
Xiu-Yuan Li ◽  
Li-Mei Bu ◽  
Hong-Xia Bu ◽  
...  
Keyword(s):  
Multidimensional Scaling ◽  
High Speed ◽  
Optimization Problem ◽  
Genome Structure ◽  
3D Structure ◽  
Restriction Enzymes ◽  
Contact Map ◽  
Scaling Method ◽  
Chromosome Conformation ◽  
Contact Frequency

AbstractChromatin 3D conformation plays important roles in regulating gene or protein functions. High-throughout chromosome conformation capture (3C)-based technologies, such as Hi-C, have been exploited to acquire the contact frequencies among genomic loci at genome-scale. Various computational tools have been proposed to recover the underlying chromatin 3D structures from in situ Hi-C contact map data. As connected residuals in a polymer, neighboring genomic loci have intrinsic mutual dependencies in building a 3D conformation. However, current methods seldom take this feature into account. We present a method called ShNeigh, which combines the classical MDS technique with local dependence of neighboring loci modelled by a Gaussian formula, to infer the best 3D structure from noisy and incomplete contact frequency matrices. The results obtained on simulations and real Hi-C data showed, while keeping the high-speed nature of classical MDS, ShNeigh is more accurate and robust than existing methods, especially for sparse contact maps. A Matlab implementation of the proposed method is available at https://github.com/fangzhen-li/ShNeigh.Author summaryWe propose a new method to infer a consensus 3D genome structure from a Hi-C contact map. The novelty of our method is that it takes into accounts the adjacency of genomic loci along chromosomes. Specifically, the proposed method penalizes the optimization problem of the classical multidimensional scaling method with a smoothness constraint weighted by a function of the genomic distance between the pairs of genomic loci. We demonstrate this optimization problem can still be solved efficiently by a classical multidimensional scaling method. We then show that the method can recover stable structures in high noise settings. We also show that it can reconstruct similar structures from data obtained using different restriction enzymes.

scholarly journals Advantages of using graph databases to explore chromatin conformation capture experiments

2021 ◽  
Vol 22 (S2) ◽  
Author(s):  
Daniele D’Agostino ◽  
Pietro Liò ◽  
Marco Aldinucci ◽  
Ivan Merelli
Keyword(s):  
Web Application ◽  
High Throughput Sequencing ◽  
Cell Types ◽  
Graph Database ◽  
Graph Databases ◽  
Sources Of Information ◽  
Chromosome Conformation ◽  
Wide Scale ◽  
User Friendly ◽  
Different Cell Types

Abstract Background High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of DNA interactions and 3D chromosome folding at the genome-wide scale. Usually, these data are represented as matrices describing the binary contacts among the different chromosome regions. On the other hand, a graph-based representation can be advantageous to describe the complex topology achieved by the DNA in the nucleus of eukaryotic cells. Methods Here we discuss the use of a graph database for storing and analysing data achieved by performing Hi-C experiments. The main issue is the size of the produced data and, working with a graph-based representation, the consequent necessity of adequately managing a large number of edges (contacts) connecting nodes (genes), which represents the sources of information. For this, currently available graph visualisation tools and libraries fall short with Hi-C data. The use of graph databases, instead, supports both the analysis and the visualisation of the spatial pattern present in Hi-C data, in particular for comparing different experiments or for re-mapping omics data in a space-aware context efficiently. In particular, the possibility of describing graphs through statistical indicators and, even more, the capability of correlating them through statistical distributions allows highlighting similarities and differences among different Hi-C experiments, in different cell conditions or different cell types. Results These concepts have been implemented in NeoHiC, an open-source and user-friendly web application for the progressive visualisation and analysis of Hi-C networks based on the use of the Neo4j graph database (version 3.5). Conclusion With the accumulation of more experiments, the tool will provide invaluable support to compare neighbours of genes across experiments and conditions, helping in highlighting changes in functional domains and identifying new co-organised genomic compartments.

scholarly journals Recovery of photoacoustic images based on accurate ultrasound positioning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yinhao Pan ◽  
Ningbo Chen ◽  
Liangjian Liu ◽  
Chengbo Liu ◽  
Zhiqiang Xu ◽  
...  
Keyword(s):  
Biological Tissue ◽  
Large Field ◽  
Photoacoustic Microscopy ◽  
Label Free ◽  
Imaging Data ◽  
Motor Speed ◽  
Image Correction ◽  
Microscopy Imaging ◽  
Scanning Method

AbstractPhotoacoustic microscopy is an in vivo imaging technology based on the photoacoustic effect. It is widely used in various biomedical studies because it can provide high-resolution images while being label-free, safe, and harmless to biological tissue. Polygon-scanning is an effective scanning method in photoacoustic microscopy that can realize fast imaging of biological tissue with a large field of view. However, in polygon-scanning, fluctuations of the rotating motor speed and the geometric error of the rotating mirror cause image distortions, which seriously affect the photoacoustic-microscopy imaging quality. To improve the image quality of photoacoustic microscopy using polygon-scanning, an image correction method is proposed based on accurate ultrasound positioning. In this method, the photoacoustic and ultrasound imaging data of the sample are simultaneously obtained, and the angle information of each mirror used in the polygon-scanning is extracted from the ultrasonic data to correct the photoacoustic images. Experimental results show that the proposed method can significantly reduce image distortions in photoacoustic microscopy, with the image dislocation offset decreasing from 24.774 to 10.365 μm.

scholarly journals SPARC Data Structure: Rationale and Design of a FAIR Standard for Biomedical Research Data

2021 ◽  
Author(s):  
Anita Bandrowski ◽  
Jeffrey S. Grethe ◽  
Anna Pilko ◽  
Tom Gillespie ◽  
Gabi Pine ◽  
...  
Keyword(s):  
Data Structure ◽  
Biomedical Research ◽  
Large Scale ◽  
Open Data ◽  
Cell Types ◽  
Research Data ◽  
Imaging Data ◽  
Organ Specific ◽  
The Rich ◽  
Automated Tools

AbstractThe NIH Common Fund’s Stimulating Peripheral Activity to Relieve Conditions (SPARC) initiative is a large-scale program that seeks to accelerate the development of therapeutic devices that modulate electrical activity in nerves to improve organ function. Integral to the SPARC program are the rich anatomical and functional datasets produced by investigators across the SPARC consortium that provide key details about organ-specific circuitry, including structural and functional connectivity, mapping of cell types and molecular profiling. These datasets are provided to the research community through an open data platform, the SPARC Portal. To ensure SPARC datasets are Findable, Accessible, Interoperable and Reusable (FAIR), they are all submitted to the SPARC portal following a standard scheme established by the SPARC Curation Team, called the SPARC Data Structure (SDS). Inspired by the Brain Imaging Data Structure (BIDS), the SDS has been designed to capture the large variety of data generated by SPARC investigators who are coming from all fields of biomedical research. Here we present the rationale and design of the SDS, including a description of the SPARC curation process and the automated tools for complying with the SDS, including the SDS validator and Software to Organize Data Automatically (SODA) for SPARC. The objective is to provide detailed guidelines for anyone desiring to comply with the SDS. Since the SDS are suitable for any type of biomedical research data, it can be adopted by any group desiring to follow the FAIR data principles for managing their data, even outside of the SPARC consortium. Finally, this manuscript provides a foundational framework that can be used by any organization desiring to either adapt the SDS to suit the specific needs of their data or simply desiring to design their own FAIR data sharing scheme from scratch.

scholarly journals Cell-Selective Regulation of CFTR Gene Expression: Relevance to Gene Editing Therapeutics

Genes ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 235 ◽  
Author(s):  
Hannah Swahn ◽  
Ann Harris
Keyword(s):  
Gene Expression ◽  
Cystic Fibrosis ◽  
Gene Editing ◽  
Genetic Diseases ◽  
3D Structure ◽  
Three Dimensional ◽  
Cell Types ◽  
Regulatory Elements ◽  
Cftr Gene ◽  
Specific Expression

The cystic fibrosis transmembrane conductance regulator (CFTR) gene is an attractive target for gene editing approaches, which may yield novel therapeutic approaches for genetic diseases such as cystic fibrosis (CF). However, for gene editing to be effective, aspects of the three-dimensional (3D) structure and cis-regulatory elements governing the dynamic expression of CFTR need to be considered. In this review, we focus on the higher order chromatin organization required for normal CFTR locus function, together with the complex mechanisms controlling expression of the gene in different cell types impaired by CF pathology. Across all cells, the CFTR locus is organized into an invariant topologically associated domain (TAD) established by the architectural proteins CCCTC-binding factor (CTCF) and cohesin complex. Additional insulator elements within the TAD also recruit these factors. Although the CFTR promoter is required for basal levels of expression, cis-regulatory elements (CREs) in intergenic and intronic regions are crucial for cell-specific and temporal coordination of CFTR transcription. These CREs are recruited to the promoter through chromatin looping mechanisms and enhance cell-type-specific expression. These features of the CFTR locus should be considered when designing gene-editing approaches, since failure to recognize their importance may disrupt gene expression and reduce the efficacy of therapies.

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