Jie Chen1, Alfred O Hero2, Indika Rajapakse3. 1. CIAIC, School of Marine Science and Technology, Northwestern Polytechnical University, China Department of Electrical Engineering and Computer Science Department of Computational Medicine & Bioinformatics, Medical School. 2. Department of Electrical Engineering and Computer Science Department of Biomedical Engineering Department of Statistics. 3. Department of Computational Medicine & Bioinformatics, Medical School Department of Mathematics, College of Literature, Science and the Arts, University of Michigan, Ann Arbor, MI, USA.
Abstract
MOTIVATION: Topological domains have been proposed as the backbone of interphase chromosome structure. They are regions of high local contact frequency separated by sharp boundaries. Genes within a domain often have correlated transcription. In this paper, we present a computational efficient spectral algorithm to identify topological domains from chromosome conformation data (Hi-C data). We consider the genome as a weighted graph with vertices defined by loci on a chromosome and the edge weights given by interaction frequency between two loci. Laplacian-based graph segmentation is then applied iteratively to obtain the domains at the given compactness level. Comparison with algorithms in the literature shows the advantage of the proposed strategy. RESULTS: An efficient algorithm is presented to identify topological domains from the Hi-C matrix. AVAILABILITY AND IMPLEMENTATION: The Matlab source code and illustrative examples are available at http://bionetworks.ccmb.med.umich.edu/ CONTACT: : indikar@med.umich.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Topological domains have been proposed as the backbone of interphase chromosome structure. They are regions of high local contact frequency separated by sharp boundaries. Genes within a domain often have correlated transcription. In this paper, we present a computational efficient spectral algorithm to identify topological domains from chromosome conformation data (Hi-C data). We consider the genome as a weighted graph with vertices defined by loci on a chromosome and the edge weights given by interaction frequency between two loci. Laplacian-based graph segmentation is then applied iteratively to obtain the domains at the given compactness level. Comparison with algorithms in the literature shows the advantage of the proposed strategy. RESULTS: An efficient algorithm is presented to identify topological domains from the Hi-C matrix. AVAILABILITY AND IMPLEMENTATION: The Matlab source code and illustrative examples are available at http://bionetworks.ccmb.med.umich.edu/ CONTACT: : indikar@med.umich.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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