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WIREs Syst Biol Med
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Computational methods for analyzing and modeling genome structure and organization

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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
An exemplar Hi‐C contact map highlighting various features identifiable at different resolutions. (a) A genome‐wide contact matrix derived from a Hi‐C experiment can be visualized using a heat map such as the one shown here derived from a Hi‐C experiment on IMR90 cells (Rao et al., ). Note the prominent diagonal representing an enrichment of intrachromosomal contacts. Also discernible are enhanced levels of interchromosomal interaction among the smaller chromosomes. (b) As in (a), but only for chromosome 2 (boxed in (a)) with the upper triangle showing the Pearson correlation of the observed/expected matrix. This transformed matrix is subjected to eigenvalue decomposition to obtain the first principal component that captures the compartmentalization of the chromosome, as illustrated in the track on the right of the matrix. (c) A schematic representation of the contact decay curve obtained by considering the average number of contacts within each diagonal row of bins moving away from the main diagonal. The curve captures the exponential decay in contacts with respect to genomic distance. Note that this decay curve is shown in a reverse orientation to that typically presented, in order to give the reader a better intuition of how it is derived. (d) As in (a), but for a subregion of chromosome 2 (boxed in (b)) and a resolution (50 kb) that allows for the topologically associating domains (TADs) to be discerned. (e) As in (d), but for a smaller subregion (boxed in (d)) and a resolution (5 kb) that allows for contact peaks to be discerned. All heat maps were produced using JuiceBox (Durand et al., )
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The iterative process of experimentation, hypothesis generation and confirmation of three‐dimensional (3D) genomic features, exemplified by the development of the loop‐extrusion model. (a) A Hi‐C experiment results in the production of a contact matrix, which is visualized using a heat map. Computational analysis facilitates the detection of 3D genomic features such as topologically associating domains (TADs; white lines) and loops (white circles). Observations of such 3D features, aided by integration of linear genomic features (not shown), result in the development of a hypothesis as to their origin. (b) A schematic representation of the loop‐extrusion model (described in the text), a hypothesis explaining the formation of TADs and loops. (c) A polymer model of the chromosome is built based on a hypothesis such as the loop‐extrusion model. Simulations of the polymer model then produce an ensemble of 3D structures (only three are shown here) of the chromosome. The boundary element are highlighted as orange spheres while the rest of the chromosome is in blue. (d) The ensemble of structures from loop‐extrusion model simulations are used to compute a contact map (Sanborn et al., ). This contact map is then compared to observed data (see (a)) leading to refinement of the hypothesis (see (b)) and/or details regarding its implementation, such as optimizing parameters (see (c)). Additionally, functional experiments can also be conducted to validate the hypothesis, potentially necessitating the generation and testing of new ones
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Models of Systems Properties and Processes > Mechanistic Models
Laboratory Methods and Technologies > Genetic/Genomic Methods
Analytical and Computational Methods > Computational Methods

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