Visualization and data exploration of chromosome conformation capture data using Voronoi diagrams with v3c-viz

被引:0
|
作者
Race, Alan M. [1 ]
Fuchs, Alisa [2 ,3 ]
Chung, Ho-Ryun [1 ,2 ]
机构
[1] Philipps Univ Marburg, Inst Med Bioinformat & Biostat, D-35043 Marburg, Germany
[2] Max Planck Inst Mol Genet, Epigen, D-14195 Berlin, Germany
[3] Max Delbruck Ctr, Berlin Inst Med Syst Biol, D-10115 Berlin, Germany
关键词
PRINCIPLES; NUCLEOSOME; GENOME;
D O I
10.1038/s41598-023-49179-x
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Chromosome conformation capture (3C) sequencing approaches, like Hi-C or micro-C, allow for an unbiased view of chromatin interactions. Most analysis methods rely on so-called interaction matrices, which are derived from counting read pairs in bins of fixed size. Here, we propose the Voronoi diagram, as implemented in Voronoi for chromosome conformation capture data visualization (v3c-viz) to visualize 3C data. The Voronoi diagram corresponds to an adaptive-binning strategy that adapts to the local densities of points. In this way, visualization of data obtained by moderate sequencing depth pinpoint many, if not most, interesting features such as high frequency contacts. The favorable visualization properties of the Voronoi diagram indicate that the Voronoi diagram as density estimator can be used to identify high frequency contacts at a resolution approaching the typical size of enhancers and promoters. v3c-viz is available at https://github.com/imbbLab/v3c-viz.
引用
收藏
页数:10
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  • [29] [1] A. Freeman, "SAR calibration: An overview," IEEE Trans. Geosci. Remote Sens., vol. 30, no. 6, pp. 1107-1121, Nov. 1992. [2] Y. K. Chan and V. Koo, "An introduction to synthetic aperture radar (SAR)," Prog. Electromagn. Res. B, vol. 2, pp. 27-60, 2008. [3] S. Adeli, "Wetland monitoring using SAR data: A meta-analysis and comprehensive review," Remote Sens., vol. 12, no. 14, pp. 2190-2217, 2020. [4] M. Tello, C. López-Martinez, and J. J. Mallorqui, "A novel algorithm for ship detection in SAR imagery based on the wavelet transform," IEEE Geosci. Remote Sens. Lett., vol. 2, no. 2, pp. 201-205, Apr. 2005. [5] M. Liao, C. Wang, Y. Wang, and L. Jiang, "Using SAR images to detect ships from sea clutter," IEEE Geosci. Remote Sens. Lett., vol. 5, no. 2, pp. 194-198, Apr. 2008. [6] S. Song, B. Xu, and J. Yang, "SAR target recognition via supervised discriminative dictionary learning and sparse representation of the SAR-HOG feature," Remote Sens., vol. 8, no. 8, pp. 683-703, 2016.
    Chen, Jinyue
    Wu, Youming
    Dai, Wei
    Diao, Wenhui
    Li, Yang
    Gao, Xin
    Sun, Xian
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2025, 18 : 8659 - 8671