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      Quantifying the similarity of topological domains across normal and cancer human cell types

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      Bioinformatics
      Oxford University Press

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          Abstract

          Motivation

          Three-dimensional chromosome structure has been increasingly shown to influence various levels of cellular and genomic functions. Through Hi-C data, which maps contact frequency on chromosomes, it has been found that structural elements termed topologically associating domains (TADs) are involved in many regulatory mechanisms. However, we have little understanding of the level of similarity or variability of chromosome structure across cell types and disease states. In this study, we present a method to quantify resemblance and identify structurally similar regions between any two sets of TADs.

          Results

          We present an analysis of 23 human Hi-C samples representing various tissue types in normal and cancer cell lines. We quantify global and chromosome-level structural similarity, and compare the relative similarity between cancer and non-cancer cells. We find that cancer cells show higher structural variability around commonly mutated pan-cancer genes than normal cells at these same locations.

          Availability and implementation

          Software for the methods and analysis can be found at https://github.com/Kingsford-Group/localtadsim

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          Most cited references13

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Breaking TADs: How Alterations of Chromatin Domains Result in Disease.

            Spatial organization is an inherent property of the vertebrate genome to accommodate the roughly 2m of DNA in the nucleus of a cell. In this nonrandom organization, topologically associating domains (TADs) emerge as a fundamental structural unit that is thought to guide regulatory elements to their cognate promoters. In this review we summarize the most recent findings about TADs and the boundary regions separating them. We discuss how the disruption of these structures by genomic rearrangements can result in gene misexpression and disease.
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              Functional implications of genome topology.

              Although genomes are defined by their sequence, the linear arrangement of nucleotides is only their most basic feature. A fundamental property of genomes is their topological organization in three-dimensional space in the intact cell nucleus. The application of imaging methods and genome-wide biochemical approaches, combined with functional data, is revealing the precise nature of genome topology and its regulatory functions in gene expression and genome maintenance. The emerging picture is one of extensive self-enforcing feedback between activity and spatial organization of the genome, suggestive of a self-organizing and self-perpetuating system that uses epigenetic dynamics to regulate genome function in response to regulatory cues and to propagate cell-fate memory.
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                Author and article information

                Journal
                Bioinformatics
                Bioinformatics
                bioinformatics
                Bioinformatics
                Oxford University Press
                1367-4803
                1367-4811
                01 July 2018
                27 June 2018
                27 June 2018
                : 34
                : 13 , ISMB 2018 Proceedings July 6 to July 10, 2018, Chicago, IL, United States
                : i475-i483
                Affiliations
                Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA
                Author notes
                To whom correspondence should be addressed. carlk@ 123456cs.cmu.edu
                Article
                bty265
                10.1093/bioinformatics/bty265
                6022623
                29949963
                0e6dc9a0-06ce-4776-a302-3b2924a83108
                © The Author(s) 2018. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                Page count
                Pages: 9
                Funding
                Funded by: Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative
                Award ID: GBMF4554
                Funded by: US National Science Foundation
                Award ID: CCF-1256087
                Award ID: CCF-1319998
                Funded by: National Institutes of Health 10.13039/100000002
                Award ID: R01HG007104
                Award ID: R01GM122935
                Funded by: NIGMS 10.13039/100000057
                Funded by: NIH 10.13039/100000002
                Award ID: P41GM103712
                Funded by: The Shurl and Kay Curci Foundation
                Categories
                Ismb 2018–Intelligent Systems for Molecular Biology Proceedings
                Studies of Phenotypes and Clinical Applications

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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