Modeling the 3D Conformation of Genomes PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Modeling the 3D Conformation of Genomes PDF full book. Access full book title Modeling the 3D Conformation of Genomes by Guido Tiana. Download full books in PDF and EPUB format.
Author: Guido Tiana
Publisher: CRC Press
ISBN: 1351387006
Category : Science
Languages : en
Pages : 370
Get Book Here
Book Description
This book provides a timely summary of physical modeling approaches applied to biological datasets that describe conformational properties of chromosomes in the cell nucleus. Chapters explain how to convert raw experimental data into 3D conformations, and how to use models to better understand biophysical mechanisms that control chromosome conformation. The coverage ranges from introductory chapters to modeling aspects related to polymer physics, and data-driven models for genomic domains, the entire human genome, epigenome folding, chromosome structure and dynamics, and predicting 3D genome structure.
Author: Guido Tiana
Publisher: CRC Press
ISBN: 1351387006
Category : Science
Languages : en
Pages : 370
Get Book Here
Book Description
This book provides a timely summary of physical modeling approaches applied to biological datasets that describe conformational properties of chromosomes in the cell nucleus. Chapters explain how to convert raw experimental data into 3D conformations, and how to use models to better understand biophysical mechanisms that control chromosome conformation. The coverage ranges from introductory chapters to modeling aspects related to polymer physics, and data-driven models for genomic domains, the entire human genome, epigenome folding, chromosome structure and dynamics, and predicting 3D genome structure.
Author: Sharif Ahmed
Publisher:
ISBN:
Category :
Languages : en
Pages : 63
Get Book Here
Book Description
3D genome structures are important because they help us understand spatial gene regulation, transcription efficiency, genome interpretation, function implication (ENCODE), disease diagnosis, treatments and drug design. Recent study suggests that the spatial arrangement of chromosomes helps chromosomes to interact with themselves. This phenomenon convinced many researchers of the value of understanding the 3D genome structure, drawing interest to the field of genome modeling. Here we constructed 3D conformations of genomes using chromosomal contact data acquired by using the Hi-C technique. This technique is designed to determine both intra- and inter-chromosomal contacts in an unbiased manner at the whole genome scale. To construct 3D structures of any chromosome we only consider intrachromosomal contacts or interactions. We can think of a chromosome as a necklace with beads threaded together on a string. Now in our case, we can cut the whole chromosome into chunks that are one megabase (1Mb) in size, which gives us loci that we can treat as beads. Using our approach we can construct 3D structures of genomes at 1Mb scale by plotting the 3D coordinates of each 1Mb region and then connecting them. In a 3D modeling problem, it is crucial to initialize the starting model before using any optimization technique. So at first we try to initialize the coordinates using growth step which provides a probabilistic approach in determining their location. Chromatin that is not compressed into the dense chromosome form still resides in a globular shaped nucleus, suggesting a spherical model as a starting model for the smaller chromosomes. For larger chromosomes, former initialization is used as they have more regions for a specific resolution (i.e. 1Mb). After initialization, we apply two widely known optimization techniques, simulated annealing and genetic algorithms. Our novel scoring function allows optimization procedures to satisfy more intra-chromosomal contacts and non-contacts as well as some additional constraints. To perturb the position of the regions, as is mandatory for modeling optimization algorithms, the adaptation technique is used. This technique tries to fix the position of each region with high contact or noncontact satisfaction. This approach is inspired by similar work for proteins and can generate an ensemble of structures very quickly. The models generated are then compared with the published results of the MCMC5C method. It is found that in all cases our method produces models that are superior to the MCMC5C models. We present some visualization techniques to show how many contacts/non-contacts are satisfied/unsatisfied and also derive some simple yet powerful scoring measurements to evaluate widely known long range contacts. The robustness of the method is measured by convergence testing and recovering capability. Finally, we examine our final model for compartment features that Lieberman et al. suggested exist in chromosomes 14 and 22. We found those features to exist in our models as well, which validates our method.
Author: Yifeng Qi (Scientist in chemistry)
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
This thesis is organized as follows. In the first chapter, we introduce a computational model to simulate chromatin structure and dynamics. The model defines chromatin states by taking one-dimensional genomics and epigenomics data as input and quantitatively learns interacting patterns between these states using experimental contact data. Once learned, the model is able to make de novo predictions of 3D chromatin structures at five-kilo-base resolution across different cell types. The manuscript associated with this study is published in PLoS Computational Biology, 15.6, e1007024 (2019).
Author: Maxime Guiffo Pouokam
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Genomes from bacteria to eukaryotes are intricately organized by the mutual interplay between the three-dimensional (3D) folding of their genome and their functional cell activities. In this dissertation, we propose exhaustive computational and statistical approaches to analyze chromosome conformation capture (CCC) data to investigate the 3D structure of the genome at both the level of CCC interaction counts between genomic loci and that of the 3D physical reconstruction structure. In this work, we use the yeast Saccharomyces cerevisiae (S. cerevisiae) as a model system. Our first result identifies the Rabl configuration, an evolutionary conserved feature of the 3D nuclear organization, characterized by the clustering of centromeres on one side of the nuclear envelope and the telomeres at the antipodal side, as an essential player in the simplification of the entanglement of chromatin fibers. In our approach, we introduced a new geometrical invariant termed the linking proportion that can capture the entanglement between pairs of chromosomes. Next, we showcase a novel approach of statistical topology whereby agreement between chromatin configuration reconstructions, which includes reproducibility of chromatin con- figurations and evaluation of chromatin reconstruction algorithms, can be evaluated. Our proposed approach makes use of the linking proportion together with statistical methods in inference to reach the important conclusion that the multidimensional scaling methods fails to preserve chromosomal topology. Finally, we present Smooth3D, a novel approach of inferring the 3D genome configuration structure from the CCC interaction counts based on cubic spline approximation. Smooth3D produces the 3D chromosomal trajectory from the CCC interactions counts via B-spline curve fitting using a least-squares algorithm. Our method estimates both the parameter of the transfer counts to distance function and the 3D chromosomal trajectory.
Author: Veronica van Heyningen
Publisher: Academic Press
ISBN: 0080877818
Category : Science
Languages : en
Pages : 415
Get Book Here
Book Description
Long-Range Control of Gene Expression covers the current progress in understanding the mechanisms for genomic control of gene expression, which has grown considerably in the last few years as insight into genome organization and chromatin regulation has advanced. Discusses the evolution of cis-regulatory sequences in drosophila Includes information on genomic imprinting and imprinting defects in humans Includes a chapter on epigenetic gene regulation in cancer
Author: Tarak Shisode
Publisher:
ISBN:
Category : Applied mathematics
Languages : en
Pages : 0
Get Book Here
Book Description
This dissertation summarizes my graduate work on the structure and organization of mouse genome during preimplantation development. My research is divided into three different areas, which I will discuss in turn. To begin, I will discuss my collaborative work on parental-to-embryo switch of chromosome organization during critical stages of early development. Notably, both paternal and maternal epigenomes undergo significant modifications following fertilization. Recent epigenomic studies have revealed the extraordinary chromatin landscapes found in oocytes, sperm, and early preimplantation embryos, including atypical histone modification patterns and differences in chromosome organization and accessibility. However, these studies reached polar opposite conclusions: the global absence of local topological-associated domains (TADs) in gametes and their appearance in the embryo versus the zygote's pre-existence of TADs and loops. The issues of whether parental structures can be inherited in the newly formed embryo and how these structures may be related to allele-specific gene regulation remain unresolved. To address this question, we use an optimized single cell high-throughput chromosome conformation capture (HiC) protocol to map genomic interactions for each parental genome (including the X chromosome) during mouse preimplantation. We integrate chromosome organization with allelic expression states and chromatin marks and demonstrate that after fertilization, higher-order chromatin structure is associated with an allele specific enrichment of histone H3 lysine 27 methylation. These early parental-specific domains are associated with gene repression and contribute to parentally biased gene expression-including newly described transiently imprinted loci. Additionally, we observe that these domains emerge in a non-parental-specific manner during the second wave of genome assembly. Finally, we discover that these domains are lost as genes are silenced on the paternal X chromosome but persist in regions that are not inactivated by the X chromosome. These findings highlight the complexities of three-dimensional genome organization and gene expression dynamics during early development. Second, I will discuss my work on some common and cell type-specific themes of higher order chromatin arrangements during mouse preimplantation development. Mapping the spatial organization of the genome is critical for comprehending its regulatory function in health, disease, and development. Our findings demonstrate an extraordinary amount of parent-specific chromosome choreography during the concatenation of two genomes. After fertilization, we observe an abrupt emergence of a Rabl-like configuration and a high head-to-head and tail-to-tail alignment of the chromosomes, which are gradually lost by the 64-cell stage. Additionally, the characteristics and marks of active and inactive chromatin exhibit a distinct radial profile across developmental stages and the genome. Finally, in addition to the well-known hallmarks of genome organization, we observe a preferential organization of chromosome territories - which call the "Territome". We were able to distinguish cell types based on the radial and relative positioning of the chromosomes in the 3D reconstructions. This suggest that interchromosomal interactions are just as critical for defining chromatin architecture and cellular identity as intrachromosomal interactions. Our findings establish a novel criterion for classifying cells when other hallmarks are difficult to quantify or when transcriptomics data is unavailable, thus paving a whole new way of looking at cells and learning how they function. Finally, with advances in experimental and theoretical approaches for generating single cell chromatin conformation capture assays, elucidating the genome's structure-function relationship has become a highly active area of research. Numerous computational methods have been developed to infer the genome's three-dimensional organization using Hi-C data from single cells. This is referred to as the three-dimensional genome reconstruction problem in formal terms (3D-GRP). While numerous methods exist for predicting the three-dimensional structure of a single genomic region, chromosome, or genome, the reconstructed models do not satisfy all of the input constraints. To address this, we present CUT & GROW, a method for improving the accuracy of three-dimensional chromosome structure inference using an iterative importance sampling strategy. CUT & GROW refines the structure of a three-dimensional chromosome (or genome) model by regrowing fragments of varying sizes locally, satisfying the majority of input constraints and providing a more precise view of the structure-function relationship
Author: Ryuichiro Nakato
Publisher: Humana
ISBN: 9781071641354
Category : Science
Languages : en
Pages : 0
Get Book Here
Book Description
This volume covers the latest methods and analytical approaches used to study the computational analysis of three-dimensional (3D) genome structure. The chapters in this book are organized into six parts. Part One discusses different NGS assays and the regulatory mechanism of 3D genome folding by SMC complexes. Part Two presents analysis workflows for Hi-C and Micro-C in different species, including human, mouse, medaka, yeast, and prokaryotes. Part Three covers methods for chromatin loop detection, sub-compartment detection, and 3D feature visualization. Part Four explores single-cell Hi-C and the cell-to-cell variability of the dynamic 3D structure. Parts Five talks about the analysis of polymer modelling to simulate the dynamic behavior of the 3D genome structure, and Part Six looks at 3D structure analysis using other omics data, including prediction of 3D genome structure from the epigenome, double-strand break-associated structure, and imaging-based 3D analysis using seqFISH. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and tools, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Computational Methods for 3D Genome Analysis: Methods and Protocols is a valuable resource for researchers interested in using computational methods to further their studies in the nature of 3D genome organization.
Author: Silvio Bicciato
Publisher: Humana
ISBN: 9781071613924
Category : Science
Languages : en
Pages : 0
Get Book Here
Book Description
This volume details a comprehensive set of methods and tools for Hi-C data processing, analysis, and interpretation. Chapters cover applications of Hi-C to address a variety of biological problems, with a specific focus on state-of-the-art computational procedures adopted for the data analysis. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Hi-C Data Analysis: Methods and Protocols aims to help computational and molecular biologists working in the field of chromatin 3D architecture and transcription regulation.
Author: Anastasiya Belyaeva
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Biological processes from differentiation to disease progression are governed by gene regulatory mechanisms. Currently large-scale omics and imaging data sets are being collected to characterize gene regulation at every level. Such data sets present new opportunities and challenges for extracting biological insights and elucidating the gene regulatory logic of cells. In this thesis, I present computational methods for the analysis and integration of various data types used for cell profiling. Specifically, I focus on analyzing and linking gene expression with the 3D organization of the genome. First, I describe methodologies for elucidating gene regulatory mechanisms by considering multiple data modalities. I design a computational framework for identifying colocalized and coregulated chromosome regions by integrating gene expression and epigenetic marks with 3D interactions using network analysis. Then, I provide a general framework for data integration using autoencoders and apply it for the integration and translation between gene expression and chromatin images of naive T-cells. Second, I describe methods for analyzing single modalities such as contact frequency data, which measures the spatial organization of the genome, and gene expression data. Given the important role of the 3D genome organization in gene regulation, I present a methodology for reconstructing the 3D diploid conformation of the genome from contact frequency data. Given the ubiquity of gene expression data and the recent advances in single-cell RNA-sequencing technologies as well as the need for causal modeling of gene regulatory mechanisms, I then describe an algorithm as well as a software tool, difference causal inference (DCI), for learning causal gene regulatory networks from gene expression data. DCI addresses the problem of directly learning differences between causal gene regulatory networks given gene expression data from two related conditions. Finally, I shift my focus from basic biology to drug discovery. Given the current COVID19 pandemic, I present a computational drug repurposing platform that enables the identification of FDA approved compounds for drug repurposing and investigation of potential causal drug mechanisms. This framework relies on identifying drugs that reverse the signature of the infection in the space learned by an autoencoder and then uses causal inference to identify putative drug mechanisms.
Author: Tamar Schlick
Publisher: Springer Science & Business Media
ISBN: 0387224645
Category : Science
Languages : en
Pages : 669
Get Book Here
Book Description
Very broad overview of the field intended for an interdisciplinary audience; Lively discussion of current challenges written in a colloquial style; Author is a rising star in this discipline; Suitably accessible for beginners and suitably rigorous for experts; Features extensive four-color illustrations; Appendices featuring homework assignments and reading lists complement the material in the main text
