Table 3.
Tools to analyze Hi-C data at different structural levels.
| Tool | Function/algorithm/download link | Resolution | Reference |
|---|---|---|---|
| loop Detection | loop detection | 1 ~ 10 kb | |
| HiCCUPS | [18] | ||
| HOMER | [115] | ||
| GOTHiC | [116] | ||
| Fit-Hi-C | [117] | ||
| HiC-DC | [118] | ||
| SIP | [119] | ||
| cLoops/cDBSCAN | [120] | ||
| Mustache | [121] | ||
| Chicago | [122] | ||
| PSYCHIC | [123] | ||
| diffHiC | differential analysis | [124] | |
| FIND | [125] | ||
| HICcompare | [126] | ||
| TADs Detection | ~40 kb | ||
| HMM | Directionality Index | [127] | |
| DP | Dynamic programming | [128] | |
| HicSeg | Two-dimensional segmentation | [129] | |
| Arrowhead | Arrow matrix | [18] | |
| insulation score | Insulation Square Analysis | [130] | |
| DHDF | Cluster-based | [131] | |
| TopDom | IdentifyTD, evaluate quality | [132] | |
| TADtree | hierarchical TADs | [133] | |
| TADs_Identification | Spectral identification | [134] | |
| IC-Finder | Hierarchical clustering | [135] | |
| MrTADFinder | network modularity based | [136] | |
| 3DNetMod | network modularity based | [137] | |
| HiTAD | domain-based alignment | [138] | |
| rGAMP | Gaussian Mixture model and Proportion test | [139] | |
| HiCDB | local relative, insulation metric | [140] | |
| deDoc | graph structural entropy | [141] | |
| tadbit | breakpoint detection algorithm | ||
| TADBoundaryDectector | deepLearning-based | [142] | |
| EAST | Haar-based algorithm | [143] | |
| TADBD | Haar-based algorithm | [144] | |
| TADCompare | Differential TADs | [145] | |
| TADpole | hierarchy of TADs in intra-chromosomal interaction matrices | [146] | |
| SpectralTAD | Spectral cluster | [147] | |
| ClusterTAD | an unsupervised machine learning approach | [148] | |
| Matryoshka | cluster | [149] | |
| A/B compartment | |||
| PCA | A/B compartment | 100 kb | |
| HOMER | [115] | ||
| juicebox | [87] | ||
| CscoreTools | https://github.com/scoutzxb/CscoreTool | ||
| HiCPro | http://github.com/nservant/HiC-Pro | [151] | |
| 3D structure | |||
| contact-based | |||
| Gen3D | adaptation, simulated annealing, and genetic algorithm | 200 kb | [152] |
| MOGEN | Gradient ascent | 200 kb-1 Mb | [153] |
| GEM | manifold learning | 1 Mb | [114] |
| GEM-FISH | polymer model | 5 kb | [154] |
| SuperRec | multidimensional scaling | 100 kb | [155] |
| distance-based | |||
| AutoChrom3D | considering the sequencing depth | 8 kb | [73] |
| ChromSDE | semi-definite embedding approach | 500 kb-1 Mb | [103] |
| ShRec3D | Short-path algorithm | 3–150 kb | [156] |
| FisHiCal | SMACOF algorithm | 1 Mb | [100] |
| MBO | manifold optimization | unknow | [107] |
| InfMod3DGen | Gradient ascent | unknow | [104] |
| 3D-GNOME | Markov chain, Simulated annealing | 1–2 Mb | [93] |
| Chromosome3D | Simulated annealing | 500 kb-1 Mb | [97] |
| LorDG | lorentzian objective function | 500 kb-1 Mb | [111] |
| HSA | Multi-track modeling, Markov chain, Simulated annealing | 25 kb-1 Mb | [157] |
| miniMDS | Hierarchical modeling | 10–100 kb | [108] |
| TADbit | https://github.com/3DGenomes/tadbit | unknow | [94] |
| mdsga | genetic algorithm | unknow | [95] |
| ShRec3+ | two-step algorithm | 1 Mb | [112] |
| 3DMax | maximum likelihood algorithm | 1 Mb | [88] |
| Hierarchical3DGenome | Hierarchical modeling | 1–5 kb | [101] |
| EVR | Error-Vector Resultant | unknow | [99] |
| ShNeigh | Gaussian formula | unknow | [158] |
| Probability-Based | |||
| BAC, BACH-MIX | Bayesian Inference | 40 kb | [159] |
| pastis | multidimensional scaling | 100 kb-1 Mb | [90] |
| tRex | Monte Carlo sampling etc. | 1 Mb | [92] |
| PGS | simulated annealing | 50 kb-1 Mb | [159] |
| SIMBA3D | Bayesian Estimation | [160] | |
| CHROMSTRUCT 4 | Monte Carlo sampling | [161] | |
| online tools | |||
| NDB | https://ndb.rice.edu/ | [162] | |
| Csynth | https://csynth.org | [163] | |
| GSDB | sysbio.rnet.missouri.edu/3dgenome/GSDB | [164] | |
| 3D-GNOME 2.0 | 3dgnome.cent.uw.edu.pl/ | [165] | |
| 3DGD | http://3dgd. biosino.org/ | [166] | |
| 3DIV | http://3div.kr/ | [167] | |
| 3DGB | http://3dgb.cs.mcgill.ca/ | [168] |
aEach column denotes the key properties of available tools to analyze Hi-C data at different structural levels. ‘Tools’ denotes availability of open-source software for a method. ‘Function/algorithm/download link’ column denotes Function, algorithms used by a method or download link for access,’ Resolution’ column denotes the resolution of Hi-C data described in the published method’s, ‘Reference’ column denotes the references where the methods were published.