Two-dimensional segmentation for analyzing Hi-C data.
Two-dimensional segmentation for analyzing Hi-C data.
复制标题
用于分析HI-C数据的二维分割。
DOI：
10.1093/bioinformatics/btu443
复制
发表时间：
2014-09-01
期刊：
影响因子：
--
通讯作者：
Robin S
中科院分区：
文献类型：
--
作者：
Lévy-Leduc C;Delattre M;Mary-Huard T;Robin S
动机：染色体的空间构象对基因的调控和表达有很深的影响。HI-C技术允许评估基因组上任何一对基因座之间的空间接近程度。它导致数据矩阵中出现对应于(自)交互区域的块。这些块的划分对于更好地理解染色质的空间组织是至关重要的。从计算的角度来看，这导致了2D分割问题。结果：我们专注于顺式相互作用区域的检测，这些区域在观察数据中似乎是突出的。我们定义了一种块分割模型来检测这样的区域。我们证明了关于块边界的可能性的最大化可以用一维分割问题来表述，标准的动态规划适用于该问题。通过对合成数据和重采样数据的仿真研究，对所提方法的性能进行了评估。对公开数据的对比研究表明，与生物确认的地区有很好的一致性。可获得性和实施：HiCseg R包可从全面R档案网和相应作者的网页上获得。联系人：celine.levy-leduc@agroparistech.fr
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. Availability and implementation: The HiCseg R package is available from the Comprehensive R Archive Network and from the Web page of the corresponding author. Contact: celine.levy-leduc@agroparistech.fr
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影响因子：
64.5
作者：
Phillips-Cremins JE;Sauria ME;Sanyal A;Gerasimova TI;Lajoie BR;Bell JS;Ong CT;Hookway TA;Guo C;Sun Y;Bland MJ;Wagstaff W;Dalton S;McDevitt TC;Sen R;Dekker J;Taylor J;Corces VG
通讯作者：
Corces VG
影响因子：
2
作者：
Darbon, Jerome;Sigelle, Marc
通讯作者：
Sigelle, Marc
影响因子：
12.3
作者：
Fraser J;Rousseau M;Shenker S;Ferraiuolo MA;Hayashizaki Y;Blanchette M;Dostie J
通讯作者：
Dostie J
影响因子：
64.8
作者：
Nora, Elphege P.;Lajoie, Bryan R.;Schulz, Edda G.;Giorgetti, Luca;Okamoto, Ikuhiro;Servant, Nicolas;Piolot, Tristan;van Berkum, Nynke L.;Meisig, Johannes;Sedat, John;Gribnau, Joost;Barillot, Emmanuel;Bluethgen, Nils;Dekker, Job;Heard, Edith
通讯作者：
Heard, Edith
影响因子：
4.4
作者：
Lavielle, M
通讯作者：
Lavielle, M