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

Ref Aligner Mapping improvement Read filtering Bin filtering Normalization Comparison Significant differences Assembly error Significant interactions TAD/loop calling Compartment calling
Armatus 1 X
Basic4CSeq 2 X X
ChiaSig* 3 X
ChIA-PET tool* 4 X (batman, BWT-SW) X X X
ChromoR 5 X X X X
diffHic 6 X (bowtie2) iterative X X X X
HiC-inspector 7 X (bowtie) X X
HiClib/HiTC 8 X (bowtie2) Iterative X X X descriptive X
HiCNorm 9 X
HicSeg 10 X
Hi-Corrector 11 X
Hicpipe 12 X
HiCtool 13 X (bowtie2) X (HiFive) X (HiFive) X X X
HiCUP 14 X (bowtie) Pre-truncation X X
HiFive 15 X X X X X
HIPPIE 16 X (BWA) X X X
HOMER 17 X X X descriptive X
Juicer 18 X X X X X X
Mango* 19 X (bowtie) X X X X
MDM* 20 X
NuChart 21 graph X
R3Cseq 22 X X
TADbit 23 X (GEM) Post-truncation and iterative X X X descriptive X X X
TADtree 24 X
TopDom 25 X

(*) 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

[1] Filippova, D., Patro, R., & Duggal, G. (2014). Identification of alternative topological domains in chromatin, 1–11. http://doi.org/10.1186/1748-7188-9-14
[2] Walter, C., Schuetzmann, D., Rosenbauer, F., & Dugas, M. (2014). Basic4Cseq: An R/Bioconductor package for analyzing 4C-seq data. Bioinformatics , 30 (22), 3268–3269. http://doi.org/10.1093/bioinformatics/btu497
[3] Paulsen, J., Rødland, E. A., Holden, L., Holden, M., & Hovig, E. (2014). A statistical model of ChIA-PET data for accurate detection of chromatin 3D interactions. Nucleic Acids Research , 42 (18), 1–11. http://doi.org/10.1093/nar/gku738
[4] Li, G., Fullwood, M. J., Xu, H., Mulawadi, F. H., Velkov, S., Vega, V., … Sung, W. K. (2010). ChIA-PET tool for comprehensive chromatin interaction analysis with paired-end tag sequencing. Genome Biol , 11 (2), R22. http://doi.org/10.1186/gb-2010-11-2-r22
[5] Shavit, Y., & Lio’, P. (2014). Combining a wavelet change point and the Bayes factor for analysing chromosomal interaction data. Molecular bioSystems , 10 (6), 1576–85. http://doi.org/10.1039/c4mb00142g
[6] Lun, A. T. L., & Smyth, G. K. (2015). diffHic: a Bioconductor package to detect differential genomic interactions in Hi-C data. BMC Bioinformatics , 16 (1), 258. http://doi.org/10.1186/s12859-015-0683-0
[7] Castellano, G., Le Dily, F., Hermoso Pulido, A., Beato, M., & Roma, G. (2015). Hi-Cpipe: a pipeline for high-throughput chromosome capture . Retrieved from http://biorxiv.org/lookup/doi/10.1101/020636
[8] Imakaev, M. V, Fudenberg, G., McCord, R. P., Naumova, N., Goloborodko, A., Lajoie, B. R., … Mirny, L. A. (2012). Iterative correction of Hi-C data reveals hallmarks of chromosome organization. Nature Methods , 9 (10), 999–1003. http://doi.org/10.1038/nmeth.2148
[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
[10] Levy-Leduc, C., Delattre, M., Mary-Huard, T., & Robin, S. (2014). Two-dimensional segmentation for analyzing Hi-C data. Bioinformatics , 30 (17), i386–i392. http://doi.org/10.1093/bioinformatics/btu443
[11] Li, W., Gong, K., Li, Q., Alber, F., & Zhou, X. J. (2015). Hi-Corrector: A fast, scalable and memory-efficient package for normalizing large-scale Hi-C data. Bioinformatics , 31 (6), 960–962. http://doi.org/10.1093/bioinformatics/btu747
[12] Yaffe, E., & Tanay, A. (2011). Probabilistic modeling of Hi-C contact maps eliminates systematic biases to characterize global chromosomal architecture. Nature Genetics , 43 (11), 1059–65. http://doi.org/10.1038/ng.947
[13] Riccardo Calandrelli & Qiuyang Wu. www.genomegitar.org
[14] Wingett, S., Ewels, P., Furlan-Magaril, M., Nagano, T., Schoenfelder, S., Fraser, P., & Andrews, S. (2015). HiCUP: pipeline for mapping and processing Hi-C data. F1000Research , 4 , 1310. http://doi.org/10.12688/f1000research.7334.1
[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
[16] Hwang, Y. C., Lin, C. F., Valladares, O., Malamon, J., Kuksa, P. P., Zheng, Q., … Wang, L. S. (2015). HIPPIE: A high-throughput identification pipeline for promoter interacting enhancer elements. Bioinformatics , 31 (8), 1290–1292. http://doi.org/10.1093/bioinformatics/btu801
[17] Heinz, S., Benner, C., Spann, N., Bertolino, E., Lin, Y. C., Laslo, P., … Glass, C. K. (2010). Simple Combinations of Lineage-Determining Transcription Factors Prime cis-Regulatory Elements Required for Macrophage and B Cell Identities. Molecular Cell , 38 (4), 576–589. http://doi.org/10.1016/j.molcel.2010.05.004
[18] Durand, N. C., Shamim, M. S., Machol, I., Rao, S. S. P., Huntley, M. H., Lander, E. S., & Aiden, E. L. (2016). Juicer Provides a One-Click System for Analyzing Loop-Resolution Hi-C Experiments. Cell Systems , 3 (1), 95–98. http://doi.org/10.1016/j.cels.2016.07.002
[19] Phanstiel, D. H., Boyle, A. P., Heidari, N., & Snyder, M. P. (2015). Mango: A bias-correcting ChIA-PET analysis pipeline. Bioinformatics , 31 (19), 3092–3098. http://doi.org/10.1093/bioinformatics/btv336
[20] Niu, L., Li, G., & Lin, S. (2014). Statistical models for detecting differential chromatin interactions mediated by a protein. PLoS ONE , 9 (5). http://doi.org/10.1371/journal.pone.0097560
[21] Merelli, I., Liò, P., & Milanesi, L. (2013). NuChart: An R Package to Study Gene Spatial Neighbourhoods with Multi-Omics Annotations. PLoS ONE , 8 (9), 1–13. http://doi.org/10.1371/journal.pone.0075146
[22] Thongjuea, S., Stadhouders, R., Grosveld, F. G., Soler, E., & 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 Research , 41 (13), 1–12. http://doi.org/10.1093/nar/gkt373
[23] Serra, F., Baù, D., Filion, G., & Marti-Renom, M. A. (2016). Structural features of the fly chromatin colors revealed by automatic three-dimensional modeling. bioRxiv , 1–29. http://doi.org/10.1101/036764
[24] Weinreb, C., & Raphael, B. J. (2016). Identification of hierarchical chromatin domains. Bioinformatics , 32 (11), 1601–1609. http://doi.org/10.1093/bioinformatics/btv485
[25] Shin, H., Shi, Y., Dai, C., Tjong, H., Gong, K., Alber, F., & Zhou, X. J. (2015). TopDom: An efficient and deterministic method for identifying topological domains in genomes. Nucleic Acids Research , 44 (7), 1–13. http://doi.org/10.1093/nar/gkv1505

3D modeling based on Hi-C

ref Strategy Population-based Representation Physical Restraints
AutoChrom3D 1 restraint-based no Point particles
Ay et al. and Duan et al. 2, 3 restraint-based resampling Spheres Excl, Bond
BACH 4 Probabilistic yes Point particles
ChromSDE 5 Metric MDS, Semi-definite programming, optimization no Point particles
FisHiCal 6 local stress minimization MDS no Point particles
Gen3D 7 restraint-based yes Point-particles
Giorgetti et al. 8 restraint-based yes Spheres Excl
HSA 9 restraint-based no Point particles
InfMod3DGen 10 restraint-based yes Point particles
Kalhor et al. and Tjong et al. 11, 12 restraint-based (contact-based) yes Spheres Excl
MCMC5C 13 restraint-based yes Point particles
Meluzzi and Arya 14 restraint-based resampling Spheres Excl, Bond, Bend
PASTIS 15 restraint-based yes Point particles
ShRec3D 16 MDS no Point particles
RPR 17 Recurrence plot, MDS no Point particles
TADbit 18 restraint-based resampling Spheres Excl, Bond
Tokuda et al. 19 restraint-based resampling Spheres Excl, Bond
tPam 20 Probabilistic no Point particles

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

[1] Peng, C., Fu, L.-Y., Dong, P.-F., Deng, Z.-L., Li, J.-X., Wang, X.-T., & Zhang, H.-Y. (2013). The sequencing bias relaxed characteristics of Hi-C derived data and implications for chromatin 3D modeling. Nucleic Acids Research , 41 (19), e183. http://doi.org/10.1093/nar/gkt745
[2] Ay, F., Bunnik, E. M., Varoquaux, N., Bol, S. M., Prudhomme, J., Vert, J.-P., … 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 Research , 24 (6), 974–88. http://doi.org/10.1101/gr.169417.113
[3] Duan, Z., Andronescu, M., Schutz, K., McIlwain, S., Kim, Y. J., Lee, C., … Noble, W. S. (2010). A three-dimensional model of the yeast genome. Nature , 465 (7296), 363–7. http://doi.org/10.1038/nature08973
[4] Hu, M., Deng, K., Qin, Z., Dixon, J., Selvaraj, S., Fang, J., … Liu, J. S. (2013). Bayesian Inference of Spatial Organizations of Chromosomes. PLoS Computational Biology , 9 (1), e1002893. http://doi.org/10.1371/journal.pcbi.1002893
[5] Zhang, Z., Li, G., Toh, K.-C., & Sung, W.-K. (2013). 3D chromosome modeling with semi-definite programming and Hi-C data. Journal of Computational Biology : A Journal of Computational Molecular Cell Biology , 20 (11), 831–46. http://doi.org/10.1089/cmb.2013.0076
[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
[7] Nowotny, J., Ahmed, S., Xu, L., Oluwadare, O., Chen, H., Hensley, N., … Cheng, J. (2015). Iterative reconstruction of three-dimensional models of human chromosomes from chromosomal contact data. BMC Bioinformatics , 16 (1), 338. http://doi.org/10.1186/s12859-015-0772-0
[8] Giorgetti, L., Galupa, R., Nora, E. P., Piolot, T., Lam, F., Dekker, J., … Heard, E. (2014). Predictive polymer modeling reveals coupled fluctuations in chromosome conformation and transcription. Cell , 157 , 950–963. http://doi.org/10.1016/j.cell.2014.03.025
[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
[10] Wang, S., Xu, J., & Zeng, J. (2015). Inferential modeling of 3D chromatin structure. Nucleic Acids Research , 43 (8), e54. http://doi.org/10.1093/nar/gkv100
[11] Tjong, H., Li, W., Kalhor, R., Dai, C., Hao, S., Gong, K., … Alber, F. (2016). Population-based 3D genome structure analysis reveals driving forces in spatial genome organization. Proceedings of the National Academy of Sciences of the United States of America , 113 (12), E1663-72. http://doi.org/10.1073/pnas.1512577113
[12] Kalhor, R., Tjong, H., Jayathilaka, N., Alber, F., & Chen, L. (2012). Genome architectures revealed by tethered chromosome conformation capture and population-based modeling. Nature Biotechnology , 30 (1), 90–8. http://doi.org/10.1038/nbt.2057
[13] Rousseau, M., Fraser, J., Ferraiuolo, M. a, Dostie, J., & Blanchette, M. (2011). Three-dimensional modeling of chromatin structure from interaction frequency data using Markov chain Monte Carlo sampling. BMC Bioinformatics , 12 (1), 414. http://doi.org/10.1186/1471-2105-12-414
[14] Meluzzi, D., & Arya, G. (2013). Recovering ensembles of chromatin conformations from contact probabilities. Nucleic Acids Research , 41 (1), 63–75. http://doi.org/10.1093/nar/gks1029
[16] Lesne, A., Riposo, J., Roger, P., Cournac, A., & Mozziconacci, J. (2014). 3D genome reconstruction from chromosomal contacts. Nature Methods , 4 , 10–13. http://doi.org/10.1038/nmeth.3104
[15] Varoquaux, N., Ay, F., Noble, W. S., & Vert, J.-P. (2014). A statistical approach for inferring the 3D structure of the genome. Bioinformatics (Oxford, England) , 30 (12), i26-33. http://doi.org/10.1093/bioinformatics/btu268
[17] Hirata, Y., Oda, A., Ohta, K., & Aihara, K. (2016). Three-dimensional reconstruction of single-cell chromosome structure using recurrence plots. Scientific Reports , 6 , 34982. http://doi.org/10.1038/srep34982
[18] Serra, F., Baù, D., Filion, G., & Marti-Renom, M. A. (2016). Structural features of the fly chromatin colors revealed by automatic three-dimensional modeling. bioRxiv , 1–29. http://doi.org/10.1101/036764
[19] Tokuda, N., Terada, T. P., & Sasai, M. (2012). Dynamical modeling of three-dimensional genome organization in interphase budding yeast. Biophysical Journal , 102 (2), 296–304. http://doi.org/10.1016/j.bpj.2011.12.005
[20] Park, J., & Lin, S. (2015). Statistical Inference on Three-Dimensional Structure of Genome by Truncated Poisson Architecture Model (Vol. 149, pp. 245–261). http://doi.org/10.1007/978-3-319-25433-3_15

3D model visualization and browsers


Ref

1D annotation track

1D user track

2D matrix

3D chromatin model

Feature search (i.e. gene name)

Web app

3Disease Browser

1

X

X

X

X

4DGenome Browser

2

X

X

X

down

ChromContact

3

X

X (UCSC browser)

X

X

Genome3D

4

X

X

X

GMOL

5

X

X

HiBrowse

6

X

X

X

X

HiC-3DViewer

7

X

X

X

X

Hi-C data Browser (Dekker lab)

8

X

X

Hi-C data Browser


(Yue lab)


9

X (UCSC aligned)

X

X

HiView

10

X

X

X

X

Juicebox

11

X

X

X

X

TADkit

12

X

X

X

X

X

WashU EpiGenome Browser

13

X

X

X

X

X

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

[1] 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
[2] Teng, L., He, B., Wang, J., & Tan, K. (2015). 4DGenome: A comprehensive database of chromatin interactions. Bioinformatics , 31 (15), 2560–2564. http://doi.org/10.1093/bioinformatics/btv158
[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
[5] Nowotny, J., Wells, A., Xu, L., Cao, R., Trieu, T., He, C., & Cheng, J. (2015). GMOL: An Interactive Tool for 3D Genome Structure Visualization. Nature Publishing Group , 16. http://doi.org/10.1038/srep20802
[6] Sandve, G. K., Gundersen, S., Rydbeck, H., Glad, I. K., Holden, L., Holden, M., … Hovig, E. (2010). The Genomic HyperBrowser: inferential genomics at the sequence level. Genome Biology , 11 (12), R121. http://doi.org/10.1186/gb-2010-11-12-r121
[7] Djekidel Mohamed Nadhir, Mengjie Wang, Juntao Gao, Michael Q. Zhang (2015) HiC-3DViewer: a novel tool to visualize Hi-C data in 3D space (User Manual). http://bioinfo.au.tsinghua.edu.cn/member/nadhir/HiC3DViewer
[8] http://hic.umassmed.edu/welcome/welcome.php with data from

  • Lieberman-Aiden, E., van Berkum, N. L., Williams, L., Imakaev, M. V, Ragoczy, T., Telling, A., … Dekker, J. (2009). Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science , 326 (5950), 289–93. http://doi.org/10.1126/science.1181369

[9] http://www.3dgenome.org with data from:

  • Dixon, J. R., Selvaraj, S., Yue, F., Kim, A., Li, Y., Shen, Y., … Ren, B. (2012). Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature , 485 (7398), 376–80. http://doi.org/10.1038/nature11082
  • Rao, S. S. P., Huntley, M. H., Durand, N. C., Stamenova, E. K., Bochkov, I. D., James T. Robinson, … Lieberman-Aiden, E. (2014). A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping. Cell , 159 (7), 1665–1680. http://doi.org/10.1016/j.cell.2014.11.021
  • Dixon, J. R., Jung, I., Selvaraj, S., Shen, Y., Antosiewicz-Bourget, J. E., Lee, A. Y., … Ren, B. (2015). Chromatin architecture reorganization during stem cell differentiation. Nature , 518 (7539), 331–336. http://doi.org/10.1038/nature14222

[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
[11] Rao, S. S. P., Huntley, M. H., Durand, N. C., Stamenova, E. K., Bochkov, I. D., James T. Robinson, … Lieberman-Aiden, E. (2014). A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping. Cell , 159 (7), 1665–1680. http://doi.org/10.1016/j.cell.2014.11.021
[11′] http://www.aidenlab.org/juicebox/docs.html
[12] http://sgt.cnag.cat/3dg/tadkit/
[13] Zhou, X., Lowdon, R. F., Li, D., Lawson, H. A., Madden, P. a F., Costello, J. F., & Wang, T. (2013). Exploring long-range genome interactions using the WashU Epigenome Browser. Nature Methods , 10 (5), 375–376. http://doi.org/10.1038/nmeth.2440