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Two-dimensional segmentation for analyzing Hi-C data

Abstract : Motivation: The spatial conformation of the chromosome has a deep influence on gene regulation and expression. Hi-C technology allows the evaluation of the spatial proximity between any pair of loci along the genome. It results in a data matrix where blocks corresponding to (self-) interacting regions appear. The delimitation of such blocks is critical to better understand the spatial organization of the chromatin. From a computational point of view, it results in a 2D segmentation problem. Results: We focus on the detection of cis-interacting regions, which appear to be prominent in observed data. We define a block-wise segmentation model for the detection of such regions. We prove that the maximization of the likelihood with respect to the block boundaries can be rephrased in terms of a 1D segmentation problem, for which the standard dynamic programming applies. The performance of the proposed methods is assessed by a simulation study on both synthetic and resampled data. A comparative study on public data shows good concordance with biologically confirmed regions.
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Contributor : Céline Lévy-Leduc <>
Submitted on : Friday, July 17, 2020 - 5:35:55 PM
Last modification on : Thursday, June 17, 2021 - 3:23:34 AM

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C. Lévy-Leduc, M. Delattre, T. Mary-Huard, S. Robin. Two-dimensional segmentation for analyzing Hi-C data. Bioinformatics, Oxford University Press (OUP), 2014, 30 (17), pp.i386-i392. ⟨10.1093/bioinformatics/btu443⟩. ⟨hal-02902068⟩



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