Abstract
Background
Although significant progress has been made to map chromatin structure at unprecedented resolution and scales, we are short of tools that enable the intuitive visualization and navigation along the three-dimensional (3D) structure of chromatins. The available tools people have so far are generally script-based or present basic features that do not easily enable the integration of genomic data along with 3D chromatin structure, hence, many scientists find themselves in the obligation to hack tools designed for other purposes such as tools for protein structure study.
Methods
We present HiC-3DViewer, a new browser-based interactive tool designed to provide an intuitive environment for investigators to facilitate the 3D exploratory analysis of Hi-C data along with many useful annotation functionalities. Among the key features of HiC-3DViewer relevant to chromatin conformation studies, the most important one is the 1D-to-2D-to-3D mapping, to highlight genomic regions of interest interactively. This feature enables investigators to explore their data at different levels/angels. Additionally, investigators can superpose different genomic signals (such as ChIP-Seq, SNP) on the top of the 3D structure.
Results
As a proof of principle we applied HiC-3DViewer to investigate the quality of Hi-C data and to show the spatial binding of GATA1 and GATA2 along the genome.
Conclusions
As a user-friendly tool, HiC-3DViewer enables the visualization of inter/intra-chromatin interactions and gives users the flexibility to customize the look-and-feel of the 3D structure with a simple click. HiC-3DViewer is implemented in Javascript and Python, and is freely available at: http://bioinfo.au.tsinghua.edu.cn/member/nadhir/HiC3DViewer/. Supplementary information (User Manual, demo data) is also available at this website.
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References
Lieberman-Aiden, E., van Berkum, N. L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B. R., Sabo, P. J., Dorschner, M. O., et al. (2009) Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science, 326, 289–293
Li, G., Ruan, X., Auerbach, R. K., Sandhu, K. S., Zheng, M., Wang, P., Poh, H. M., Goh, Y., Lim, J., Zhang, J., et al. (2012) Extensive promoter-centered chromatin interactions provide a topological basis for transcription regulation. Cell, 148, 84–98
Sanyal, A., Lajoie, B. R., Jain, G. and Dekker, J. (2012) The long-range interaction landscape of gene promoters. Nature, 489, 109–113
Göndör, A. and Ohlsson, R. (2009) Chromosome crosstalk in three dimensions. Nature, 461, 212–217
Varoquaux, N., Ay, F., Noble, W. S. and Vert, J.-P. (2014) A statistical approach for inferring the 3D structure of the genome. Bioinformatics, 30, i26–i33
Baù, D., Sanyal, A., Lajoie, B. R., Capriotti, E., Byron, M., Lawrence, J. B., Dekker, J. and Marti-Renom, M. A. (2011) The three-dimensional folding of the a-globin gene domain reveals formation of chromatin globules. Nat. Struct. Mol. Biol., 18, 107–114
Wang, S., Xu, J. and Zeng, J. (2015) Inferential modeling of 3D chromatin structure. Nucleic Acids Res., 43, e54
Thongjuea, S., Stadhouders, R., Grosveld, F. G., Soler, E. and Lenhard, B. (2013) r3Cseq: an R/Bioconductor package for the discovery of long-range genomic interactions from chromosome conformation capture and next-generation sequencing data. Nucleic Acids Res., 41, e132
Phanstiel, D. H., Boyle, A. P., Araya, C. L. and Snyder, M. P. (2014) Sushi.R: flexible, quantitative and integrative genomic visualizations for publication-quality multi-panel figures. Bioinformatics, 30, 2808–2810
Schrödinger, LLC (2010) The PyMOL Molecular Graphics System, Versio1 1.3r1. Available: http://www.pymol.org
Nowotny, J., Wells, A., Xu, L., Cao, R., Trieu, T., He, C., Cheng, J. (2016) GMOL: an interactive tool for 3D genome structure visualization. Sci. Rep. 6, 20802
Asbury, T. M., Mitman, M., Tang, J. and Zheng, W. J. (2010) Genome3D: a viewer-model framework for integrating and visualizing multi-scale epigenomic information within a three-dimensional genome. BMC Bioinformatics, 11, 444
Peng, C., Fu, L.-Y., Dong, P.-F., Deng, Z.-L., Li, J.-X., Wang, X. T. and Zhang, H. Y. (2013) The sequencing bias relaxed characteristics of Hi-C derived data and implications for chromatin 3D modeling. Nucleic Acids Res., 41, e183
Teng, L., He, B., Wang, J. and Tan, K. (2015) 4DGenome: a comprehensive database of chromatin interactions. Bioinformatics, 31, 2560–2564
Dirksen, J. (2013) Learning Three.js: The JavaScript 3D Library for WebGL. Birmingham: Packt Publishing
Grinberg, M. (2014) Flask Web Development. Sebastopol: O’Reilly Media
Duan, Z., Andronescu, M., Schutz, K., McIlwain, S., Kim, Y. J., Lee, C., Shendure, J., Fields, S., Blau, C. A. and Noble, W. S. (2010) A three-dimensional model of the yeast genome. Nature, 465, 363–367
Sexton, T., Yaffe, E., Kenigsberg, E., Bantignies, F., Leblanc, B., Hoichman, M., Parrinello, H., Tanay, A. and Cavalli, G. (2012) Threedimensional folding and functional organization principles of the Drosophila genome. Cell, 148, 458–472
Rao, S. S., Huntley, M. H., Durand, N. C., Stamenova, E. K., Bochkov, I. D., Robinson, J. T., Sanborn, A. L., Machol, I., Omer, A. D., Lander, E. S., et al. (2014) A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell, 159, 1665–1680
Dixon, J. R., Selvaraj, S., Yue, F., Kim, A., Li, Y., Shen, Y., Hu, M., Liu, J. S. and Ren, B. (2012) Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature, 485, 376–380
Ay, F., Bunnik, E. M., Varoquaux, N., Bol, S. M., Prudhomme, J., Vert, J. P., Noble, W. S. and Le Roch, K. G. (2014) Three-dimensional modeling of the P. falciparum genome during the erythrocytic cycle reveals a strong connection between genome architecture and gene expression. Genome Res., 24, 974–988
Lan, X., Witt, H., Katsumura, K., Ye, Z., Wang, Q., Bresnick, E. H., Farnham, P. J. and Jin, V. X. (2012) Integration of Hi-C and ChIP-seq data reveals distinct types of chromatin linkages. Nucleic Acids Res., 40, 7690–7704
Hu, M., Deng, K., Selvaraj, S., Qin, Z., Ren, B. and Liu, J. S. (2012) HiCNorm: removing biases in Hi-C data via Poisson regression. Bioinformatics, 28, 3131–3133
Acknowledgments
This work is supported by State Key Research Development Program of China (No. 2016YFC1200303), and the National Natural Science Foundation of China (Nos. 31361163004 and 31671383). We thank Yanjian Li (Tsinghua University) for sharing his Hi-C data. MQZ was partially supported by UTD funds.
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Djekidel, M.N., Wang, M., Zhang, M.Q. et al. HiC-3DViewer: a new tool to visualize Hi-C data in 3D space. Quant Biol 5, 183–190 (2017). https://doi.org/10.1007/s40484-017-0091-8
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DOI: https://doi.org/10.1007/s40484-017-0091-8