Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA.
Bioinformatics. 2017 Jul 15;33(14):2199-2201. doi: 10.1093/bioinformatics/btx152.
Genome-wide proximity ligation based assays like Hi-C have opened a window to the 3D organization of the genome. In so doing, they present data structures that are different from conventional 1D signal tracks. To exploit the 2D nature of Hi-C contact maps, matrix techniques like spectral analysis are particularly useful. Here, we present HiC-spector, a collection of matrix-related functions for analyzing Hi-C contact maps. In particular, we introduce a novel reproducibility metric for quantifying the similarity between contact maps based on spectral decomposition. The metric successfully separates contact maps mapped from Hi-C data coming from biological replicates, pseudo-replicates and different cell types.
Source code in Julia and Python, and detailed documentation is available at https://github.com/gersteinlab/HiC-spector .
koonkiu.yan@gmail.com or mark@gersteinlab.org.
Supplementary data are available at Bioinformatics online.
基于基因组范围邻近连接的分析方法（如 Hi-C）为研究基因组的 3D 结构打开了一扇窗。在这样做的过程中，它们呈现出与传统的 1D 信号轨迹不同的数据结构。为了利用 Hi-C 接触图谱的 2D 性质，矩阵技术（如谱分析）特别有用。在这里，我们提出了 HiC-spector，这是一组用于分析 Hi-C 接触图谱的矩阵相关函数。特别是，我们引入了一种新颖的可重复性度量标准，用于根据谱分解来量化接触图谱之间的相似性。该度量标准成功地区分了来自生物重复、伪重复和不同细胞类型的 Hi-C 数据映射的接触图谱。
Julia 和 Python 中的源代码以及详细的文档可在 https://github.com/gersteinlab/HiC-spector 上获得。
koonkiu.yan@gmail.com 或 mark@gersteinlab.org。
补充数据可在 Bioinformatics 在线获得。
