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Two-dimensional segmentation for analyzing HiC 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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Celine Levy Leduc, Maud Delattre, Tristan Mary-Huard, Stephane Robin. Two-dimensional segmentation for analyzing HiC data. ECCB 2014: The 13th European Conference on Computational Biology, Sep 2014, Strasbourg, France. pp.386-392, ⟨10.1093/bioinformatics/btu443⟩. ⟨hal-01148580⟩



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