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Computational methods for analyzing and modeling genome structure and organization.

ABSTRACT: Recent advances in chromosome conformation capture technologies have led to the discovery of previously unappreciated structural features of chromatin. Computational analysis has been critical in detecting these features and thereby helping to uncover the building blocks of genome architecture. Algorithms are being developed to integrate these architectural features to construct better three-dimensional (3D) models of the genome. These computational methods have revealed the importance of 3D genome organization to essential biological processes. In this article, we review the state of the art in analytic and modeling techniques with a focus on their application to answering various biological questions related to chromatin structure. We summarize the limitations of these computational techniques and suggest future directions, including the importance of incorporating multiple sources of experimental data in building a more comprehensive model of the genome. This article is categorized under: Analytical and Computational Methods > Computational Methods Laboratory Methods and Technologies > Genetic/Genomic Methods Models of Systems Properties and Processes > Mechanistic Models.

SUBMITTER: Lin D

PROVIDER: S-EPMC6294685 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Computational methods for analyzing and modeling genome structure and organization.

Lin Dejun D Bonora Giancarlo G Yardımcı Galip Gürkan GG Noble William S WS

Wiley interdisciplinary reviews. Systems biology and medicine 20180718 1

Recent advances in chromosome conformation capture technologies have led to the discovery of previously unappreciated structural features of chromatin. Computational analysis has been critical in detecting these features and thereby helping to uncover the building blocks of genome architecture. Algorithms are being developed to integrate these architectural features to construct better three-dimensional (3D) models of the genome. These computational methods have revealed the importance of 3D gen ...[more]

PMID: 30022617

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Project description:BackgroundIn vertebrate cells, the Golgi functional subunits, mini-stacks, are linked into a tri-dimensional network. How this "ribbon" architecture relates to Golgi functions remains unclear. Are all connections between mini-stacks equal? Is the local structure of the ribbon of functional importance? These are difficult questions to address, without a quantifiable readout of the output of ribbon-embedded mini-stacks. Endothelial cells produce secretory granules, the Weibel-Palade bodies (WPB), whose von Willebrand Factor (VWF) cargo is central to hemostasis. The Golgi apparatus controls WPB size at both mini-stack and ribbon levels. Mini-stack dimensions delimit the size of VWF "boluses" whilst the ribbon architecture allows their linear co-packaging, thereby generating WPBs of different lengths. This Golgi/WPB size relationship suits mathematical analysis.ResultsWPB lengths were quantized as multiples of the bolus size and mathematical modeling simulated the effects of different Golgi ribbon organizations on WPB size, to be compared with the ground truth of experimental data. An initial simple model, with the Golgi as a single long ribbon composed of linearly interlinked mini-stacks, was refined to a collection of mini-ribbons and then to a mixture of mini-stack dimers plus long ribbon segments. Complementing these models with cell culture experiments led to novel findings. Firstly, one-bolus sized WPBs are secreted faster than larger secretory granules. Secondly, microtubule depolymerization unlinks the Golgi into equal proportions of mini-stack monomers and dimers. Kinetics of binding/unbinding of mini-stack monomers underpinning the presence of stable dimers was then simulated. Assuming that stable mini-stack dimers and monomers persist within the ribbon resulted in a final model that predicts a "breathing" arrangement of the Golgi, where monomer and dimer mini-stacks within longer structures undergo continuous linking/unlinking, consistent with experimentally observed WPB size distributions.ConclusionsHypothetical Golgi organizations were validated against a quantifiable secretory output. The best-fitting Golgi model, accounting for stable mini-stack dimers, is consistent with a highly dynamic ribbon structure, capable of rapid rearrangement. Our modeling exercise therefore predicts that at the fine-grained level the Golgi ribbon is more complex than generally thought. Future experiments will confirm whether such a ribbon organization is endothelial-specific or a general feature of vertebrate cells.

Project description:BACKGROUND:Genome-wide ligation-based assays such as Hi-C provide us with an unprecedented opportunity to investigate the spatial organization of the genome. Results of a typical Hi-C experiment are often summarized in a chromosomal contact map, a matrix whose elements reflect the co-location frequencies of genomic loci. To elucidate the complex structural and functional interactions between those genomic loci, networks offer a natural and powerful framework. RESULTS:We propose a novel graph-theoretical framework, the Corrected Gene Proximity (CGP) map to study the effect of the 3D spatial organization of genes in transcriptional regulation. The starting point of the CGP map is a weighted network, the gene proximity map, whose weights are based on the contact frequencies between genes extracted from genome-wide Hi-C data. We derive a null model for the network based on the signal contributed by the 1D genomic distance and use it to "correct" the gene proximity for cell type 3D specific arrangements. The CGP map, therefore, provides a network framework for the 3D structure of the genome on a global scale. On human cell lines, we show that the CGP map can detect and quantify gene co-regulation and co-localization more effectively than the map obtained by raw contact frequencies. Analyzing the expression pattern of metabolic pathways of two hematopoietic cell lines, we find that the relative positioning of the genes, as captured and quantified by the CGP, is highly correlated with their expression change. We further show that the CGP map can be used to form an inter-chromosomal proximity map that allows large-scale abnormalities, such as chromosomal translocations, to be identified. CONCLUSIONS:The Corrected Gene Proximity map is a map of the 3D structure of the genome on a global scale. It allows the simultaneous analysis of intra- and inter- chromosomal interactions and of gene co-regulation and co-localization more effectively than the map obtained by raw contact frequencies, thus revealing hidden associations between global spatial positioning and gene expression. The flexible graph-based formalism of the CGP map can be easily generalized to study any existing Hi-C datasets.

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Computational methods for analyzing and modeling genome structure and organization.

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Computational methods for analyzing and modeling genome structure and organization.

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