HiC-spector：用于 Hi-C 接触图谱的光谱和可重复性分析的矩阵库。
HiC-spector: a matrix library for spectral and reproducibility analysis of Hi-C contact maps.
机构信息
Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA.
出版信息
Bioinformatics. 2017 Jul 15;33(14):2199-2201. doi: 10.1093/bioinformatics/btx152.
SUMMARY
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.
AVAILABILITY AND IMPLEMENTATION
Source code in Julia and Python, and detailed documentation is available at https://github.com/gersteinlab/HiC-spector .
CONTACT
koonkiu.yan@gmail.com or mark@gersteinlab.org.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
摘要
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。
可用性和实现
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。
联系方式
koonkiu.yan@gmail.com 或 mark@gersteinlab.org。
补充信息
补充数据可在 Bioinformatics 在线获得。
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