3C Tools Comparison

These tables present a thorough, up-to-date comparison of available tools that deal with the three main steps in the analysis of Hi-C data: 1) Hi-C data processing, 2) 3D modelling based on Hi-C and 3) 3D model visualization and browsing. The tools are compared based on features that are relevant for their use, in particular in the field of multiscale complex genomics, in order to provide researchers with a concise overview on the large number of available tools.

• Hi-C data processing
• 3D modeling based on Hi-C
• 3D model visualization and browsers

If you use any data provided in this page, please cite the MuG project:

“MuG project H2020-676556 Oct. 2016 – from www.multiscalegenomics.eu”
This work uses data, tools or expertise provided by MuG (www.multiscalegenomics.eu) a project funded by the EU H2020 research and innovation programme: H2020-EINFRA-2015-1-676556

Hi-C data processing

(*) Designed for Chia-PET only.
Table adapted from: Ay, F., & Noble, W. S. (2015). Analysis methods for studying the 3D architecture of the genome. Genome Biology, 16(1), 183. http://doi.org/10.1186/s13059-015-0745-7

Completed with https://omictools.com/, http://3dgenomes.org/methods/ and recent publications.

References

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[9] Hu, M., Deng, K., Selvaraj, S., Qin, Z., Ren, B., & Liu, J. S. (2012). HiCNorm: Removing biases in Hi-C data via Poisson regression. Bioinformatics , 28 (23), 3131–3133. http://doi.org/10.1093/bioinformatics/bts570
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[15] Sauria, M. E., Phillips-Cremins, J. E., Corces, V. G., & Taylor, J. (2015). HiFive: a tool suite for easy and efficient HiC and 5C data analysis. Genome Biology , 16 (1), 237. http://doi.org/10.1186/s13059-015-0806-y
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[24] Weinreb, C., & Raphael, B. J. (2016). Identification of hierarchical chromatin domains. Bioinformatics , 32 (11), 1601–1609. http://doi.org/10.1093/bioinformatics/btv485
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3D modeling based on Hi-C

Table adapted from: Serra, F., Di Stefano, M., Spill, Y. G., Cuartero, Y., Goodstadt, M., Baù, D., & Marti-Renom, M. A. (2015). Restraint-based three-dimensional modeling of genomes and genomic domains. FEBS Letters, 589(20PartA), 2987–2995. http://doi.org/10.1016/j.febslet.2015.05.012
Completed with recent publications.
Abbreviation:

• MDS: Multi dimensional scaling
• Excl: excluded volume restraints
• Bond: neighboring restraints
• Bend: bending rigidity restraints

References

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[6] Shavit, Y., Hamey, F. K., & Lio, P. (2014). FisHiCal: An R package for iterative FISH-based calibration of Hi-C data. Bioinformatics , 30 (21), 3120–3122. http://doi.org/10.1093/bioinformatics/btu491
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[9] Zou, C., Zhang, Y., & Ouyang, Z. (2016). HSA: integrating multi-track Hi-C data for genome-scale reconstruction of 3D chromatin structure. Genome Biology , 17 (1), 40. http://doi.org/10.1186/s13059-016-0896-1
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3D model visualization and browsers

Adapted from: Li, R., Liu, Y., Li, T., & Li, C. (2016). 3Disease Browser: A Web server for integrating 3D genome and disease-associated chromosome rearrangement data. Scientific Reports, 6(May), 34651. http://doi.org/10.1038/srep34651

References

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[3] Sato, T., & Suyama, M. (2015). ChromContact: A web tool for analyzing spatial contact of chromosomes from Hi-C data. BMC Genomics , 16 (1), 1060. http://doi.org/10.1186/s12864-015-2282-x
[4] Lewis, T. E., Sillitoe, I., Andreeva, A., Blundell, T. L., Buchan, D. W. A., Chothia, C., … Orengo, C. (2015). Genome3D: Exploiting structure to help users understand their sequences. Nucleic Acids Research , 43 (D1), D382–D386. http://doi.org/10.1093/nar/gku973
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[10] Xu, Z., Zhang, G., Duan, Q., Chai, S., Zhang, B., Wu, C., … Hu, M. (2016). HiView: an integrative genome browser to leverage Hi-C results for the interpretation of GWAS variants. BMC Research Notes , 9 (1), 159. http://doi.org/10.1186/s13104-016-1947-0
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