Research article Special Issues

Towards a robust algorithm to determine topological domains from colocalization data

  • Received: 17 July 2015 Accepted: 05 September 2015 Published: 17 September 2015
  • One of the most important tasks in understanding the complex spatial organization of the genome consists in extracting information about this spatial organization, the function and structure of chromatin topological domains from existing experimental data, in particular, from genome colocalization (Hi-C) matrices. Here we present an algorithm allowing to reveal the underlying hierarchical domain structure of a polymer conformation from analyzing the modularity of colocalization matrices. We also test this algorithm on several model polymer structures: equilibrium globules, random fractal globules and regular fractal (Peano) conformations. We define what we call a spectrum of cluster borders, and show that these spectra behave strikingly di erently for equilibrium and fractal conformations, allowing us to suggest an additional criterion to identify fractal polymer conformations.

    Citation: Alexander P. Moscalets, Leonid I. Nazarov, Mikhail V. Tamm. Towards a robust algorithm to determine topological domains from colocalization data[J]. AIMS Biophysics, 2015, 2(4): 503-516. doi: 10.3934/biophy.2015.4.503

    Related Papers:

  • One of the most important tasks in understanding the complex spatial organization of the genome consists in extracting information about this spatial organization, the function and structure of chromatin topological domains from existing experimental data, in particular, from genome colocalization (Hi-C) matrices. Here we present an algorithm allowing to reveal the underlying hierarchical domain structure of a polymer conformation from analyzing the modularity of colocalization matrices. We also test this algorithm on several model polymer structures: equilibrium globules, random fractal globules and regular fractal (Peano) conformations. We define what we call a spectrum of cluster borders, and show that these spectra behave strikingly di erently for equilibrium and fractal conformations, allowing us to suggest an additional criterion to identify fractal polymer conformations.


    加载中
    [1] de Gennes PG (1979) Scaling Concepts in Polymer Physics, Cornell University Press.
    [2] Grosberg AY, Khokhlov AR (1994) Statistical Physics of Macromolecules, AIP Press.
    [3] Rubinstein M, Colby RH (2003) Polymer Physics, Oxford:OUP.
    [4] Grosberg AY, Nechaev SK, Shakhnovich EI (1988) The role of topological constraints in the kinetics of collapse of macromolecules. J Phys France 49(12):2095-2100.
    [5] Grosberg A, Rabin Y, Havlin S, et al. (1993) Crumpled globule model of the three-dimensional structure of DNA. EPL-Europhys Lett 23(5):373.
    [6] Rosa A, Everaers R (2008) Structure and dynamics of interphase chromosomes. PLoS Comput Biol 4(8):e1000153.
    [7] Lieberman-Aiden E, van Berkum NL, Williams L, et al. (2009) Comprehensive mapping of longrange interactions reveals folding principles of the human genome. Science 326(5950):289-293.
    [8] Mirny LA (2011) The fractal globule as a model of chromatin architecture in the cell. Chromosome Res 19(1):37-51.
    [9] Halverson JD, Smrek J, Kremer K, et al. (2014) From a melt of rings to chromosome territories: the role of topological constraints in genome folding. Rep Prog Phys 77(2):022601.
    [10] Grosberg AY (2014) Annealed lattice animal model and Flory theory for the melt of nonconcatenated rings: towards the physics of crumpling. Soft Matter 10:560-565. doi: 10.1039/C3SM52805G
    [11] Rosa A, Everaers R (2014) Ring polymers in the melt state: The physics of crumpling. Phys Rev Lett 112:118302. doi: 10.1103/PhysRevLett.112.118302
    [12] Nazarov LI, TammMV, Avetisov VA, et al. (2015) A statistical model of intra-chromosome contact maps. Soft Matter 11(5):1019-1025.
    [13] Tamm MV, Nazarov LI, Gavrilov AA, et al. (2015) Anomalous diffusion in fractal globules. Phys Rev Lett 114:178102. doi: 10.1103/PhysRevLett.114.178102
    [14] Bunin G, Kardar M (2015) Coalescence model for crumpled globules formed in polymer collapse. Phys Rev Lett 115:088303. doi: 10.1103/PhysRevLett.115.088303
    [15] Sachs RK, van den Engh G, Trask B, et al. (1995) A random-walk/giant-loop model for interphase chromosomes. P Natl Acad Sci 92(7):2710-2714.
    [16] Münkel C, Langowski J (1998) Chromosome structure predicted by a polymer model. Phys Rev E 57:5888-5896. doi: 10.1103/PhysRevE.57.5888
    [17] Ostashevsky J (1998) A polymer model for the structural organization of chromatin loops and minibands in interphase chromosomes. Mol Biol Cell 9(11):3031-3040.
    [18] Mateos-Langerak J, Bohn M, de Leeuw W, et al. (2009) Spatially confined folding of chromatin in the interphase nucleus. P Natl Acad Sci 106(10):3812-3817.
    [19] Iyer BVS, Arya G (2012) Lattice animal model of chromosome organization. Phys Rev E86:011911.
    [20] Barbieri M, Chotalia M, Fraser J, et al. (2012) Complexity of chromatin folding is captured by the strings and binders switch model. P Natl Acad Sci 109(40):16173-16178.
    [21] Fritsch CC, Langowski J (2010) Anomalous diffusion in the interphase cell nucleus: The effect of spatial correlations of chromatin. J Chem Phys 133(2):025101.
    [22] Fritsch CC, Langowski J (2011) Chromosome dynamics, molecular crowding, and diffusion in the interphase cell nucleus: a monte carlo lattice simulation study. Chromosome Res 19(1):63-81.
    [23] Dekker J, Rippe K, Dekker M, et al. (2002) Capturing chromosome conformation. Science 295(5558):1306-1311.
    [24] Naumova N, Imakaev M, Fudenberg G, et al. (2013) Organization of the mitotic chromosome. Science 342(6161):948-953.
    [25] Nagano T, Lubling Y, Stevens TJ, et al. (2013) Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502(7469):59-64.
    [26] Newman MEJ (2004) Analysis of weighted networks. Phys Rev E 70:056131. doi: 10.1103/PhysRevE.70.056131
    [27] Lancichinetti A, Fortunato S (2011) Limits of modularity maximization in community detection. Phys Rev E 84:066122. doi: 10.1103/PhysRevE.84.066122
    [28] Granell C, Gomez S, Arenas A (2012) Hierarchical multiresolution method to overcome the resolution limit in complex networks. Int J Bifurcat Chaos 22(07):1250171.
    [29] Arenas A, Fernandez A, Gomez S (2008) Analysis of the structure of complex networks at different resolution levels. New J Phys 10(5):053039.
    [30] Fortunato S (2010) Community detection in graphs. Phys Rep 486(3-5):75-174.
    [31] Newman MEJ (2006) Modularity and community structure in networks. P Natl Acad Sci 103(23):8577-8582.
    [32] Krzakala F, Moore C, Mossel E, et al. (2013) Spectral redemption in clustering sparse networks. P Natl Acad Sci 110(52):20935-20940.
    [33] Arenas A, Diaz-Guilera A, Kurths J, et al. (2008) Synchronization in complex networks. Phys Rep 469(3):93-153.
    [34] Sethna JP (2006) Statistical Mechanics: Entropy, Order Parameters and Complexity, Oxford:OUP.
    [35] Mezard M, Montanari A, (2009) Information, Physics, and Computation, Oxford:OUP.
  • Reader Comments
  • © 2015 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(4874) PDF downloads(1176) Cited by(1)

Article outline

Figures and Tables

Figures(8)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog