Table 1 A comparison of the methods for reconstructing 3-D chromosome and genome structure from Hi-C data
From: An Overview of Methods for Reconstructing 3-D Chromosome and Genome Structures from Hi-C Data
Algorithms | Year | Software Availability | Language | Structure Representation | In–built Normalization | IF Model Based | Methodology Based | Sampling Algorithm | Structure Based | Species, Coverage, and Resolution of Test 3C Data | Input |
|---|---|---|---|---|---|---|---|---|---|---|---|
5C3D [45] | 2009 | No |  | Points | No | Distance | Optimization | Gradient Descent | Ensemble | Human: 5C HoxA gene cluster region. | Hi–C contact matrix |
Duan et al. [66] | 2010 | No |  | Spheres | No | Distance | Optimization | IPOPT [71]– Interior–point gradient–based | Consensus | Budding yeast: Whole genome (10kb) | Hi–C contact matrix |
Tanizawa et al. [67] | 2010 | No |  | Spheres | Yes | Distance | Optimization | IPOPT– Interior–point gradient–based | Consensus | Fission yeast: Whole genome (20kb) | Hi–C contact matrix |
Bau et al. [72] | 2011 | No |  | Points | Yes | Distance | Optimization | IMP [73] – Monte Carlo(MC) sampling and simulated annealing with Metropolis criteria | Ensemble | Human: Chromosome 16 – 500–kb ENm008 domain (500kb) | Hi–C contact matrix |
MCMC5C [48] | 2011 | No | Java | Points | No | Probability | Probabilistic Modeling | Markov chain Monte Carlo (MCMC) sampling using the Metropolis–Hastings algorithm | Ensemble | Human: 5C 142kb genomic region and Hi–C Chromosome 16 – 88.4 Mb region (1Mb) | Hi–C contact matrix |
Meluzzi and Arya [74] | 2012 | No |  | Polymer | No | Contact | Optimization | Modified conjugate gradient algorithm and Brownian Dynamics simulation | Ensemble | Synthetic: 75kb – 270kb (3kb – 6kb) | Hi–C contact matrix |
Kalhor et al. [68] | 2013 | No |  | Spheres | Yes | Contact | Optimization | Conjugate gradients and molecular dynamics with simulated annealing | Population | Human: Whole genome (1Mb) | Hi–C contact matrix |
BACH [75] | 2013 | Yes | R | Points | Yes | Probability | Probabilistic Modeling | Gibbs sampler with hybrid MC, and adaptive rejection sampling (ARS) | Consensus | Mouse: All chromosomes (40kb) | Hi–C contact matrix and local genomic features (restriction enzyme cutting frequencies, GC content and sequence uniqueness) as input |
ChromSDE [76] | 2013 |  | Matlab | Points | No | Distance | Optimization | Linear and Quadratic Semi–definite programming(SDP) | Consensus | Mouse and Human: Chromosome 13 (200kb – 1Mb 40kb(chr13:21Mb–25Mb)) | Hi–C contact matrix |
AutoChrom3D [77] | 2013 | Yes | Perl | Points | Yes | Distance | Optimization | Non–linear constrained optimization | Consensus | Human: 500kb – 1MB (8kb) | Hi–C contact matrix |
PASTIS [78] | 2014 | Yes | Python | Points | No | Distance and Probability | Optimization(MDS1, MDS2) and Probabilistic Modeling (PM1,PM2) | IPOPT – interior point filter algorithm | Consensus | Mouse: All chromosomes (100kb – 1Mb, 20kb –chr1-19) | Hi–C contact matrix |
ShRec3D [79] | 2014 | Yes | Matlab | Points | No | Distance | Optimization | Shortest-path Floyd-Warshall algorithm | Consensus | Human: Chromosome 1 – 30Mbp region (3kb - 150kb) | Hi–C contact matrix |
2014 | Yes | Java | Points | No | Contact | Optimization | Gradient descent | Ensemble | Human: All chromosomes and whole genome (200kb - 1Mb) | Hi–C contact matrix | |
FisHiCal [81] | 2014 | Yes | R | Points | Yes | Distance | Optimization | SMACOF algorithm [82] | Consensus | Human: Whole genome (1Mb) | Hi–C contact matrix |
InfMod3DGen [64] | 2015 | Yes | Matlab | Polymer | No | Distance | Probabilistic Modeling | Gradient ascent | Ensemble | Yeast: All chromosomes –12.1Mb genome (10kb) | Hi–C contact matrix |
Gen3D [83] | 2015 | Yes | C++ | Points | No | Contact | Optimization | Adaptation, Simulated annealing and Genetic algorithm | Consensus | Human: All chromosomes (1Mb) | Hi–C contact matrix |
MBO [84] | 2015 | Yes | Matlab | Points | No | Distance | Optimization | Manopt – manifold optimization | Consensus | Mouse: Chromosome X (50kb - 600kb) | Hi–C contact matrix |
TADbit [85] | 2016 | Yes | Python | Spheres | Yes | Distance | Optimization | Simulated Annealing and Monte Carlo Sampling | Ensemble | Drosophila Fly: 52Mb region (10kb) | Hi–C contact matrix |
HSA [47] | 2016 | Yes | R | Points | Yes | Distance | Optimization | GLM framework with Hamiltonian dynamics with simulated annealing | Consensus | Human and Mouse: All chromosomes (25kb - 1Mb) | One or more raw contact maps or normalized Hi–C contact matrix. |
Chromosome3D [46] | 2016 | Yes | Perl | Points | No | Distance | Optimization | Distance Geometry Simulated Annealing | Ensemble | Human: All chromosomes (500kb - 1Mb) | Hi–C contact matrix |
2016 | Yes | Python | Spheres | Yes | Probability | Probabilistic Modeling | Simulated annealing/molecular dynamics | Population | Human: Whole genome (50kb - 1Mb) | Raw Hi–C contact matrix and a TAD file in bed format | |
tRex [87] | 2016 | Yes | R | Points | Yes | Probability | Probabilistic Modeling | MCMC sampling using the Metropolis–Hastings algorithm/Gibbs sampler, Hamiltonian MCMC | Ensemble | Human: Chromosome 14 and 22 (1Mb) | Hi–C contact matrix and a vector of covariates (e.g. fragment length, GC content, and mappability score) |
2016 | Web server | C++, Javascript, PHP, Python, R | Polymer | Yes | Distance | Optimization | Monte Carlo-based simulated annealing | Consensus | Human: All chromosomes (Multiscale 1-2Mb, PET (1–10kb)) | A seven or eight columns bedpe (paired–end BED format) file containing the locations and strengths of long range contact points. Use of ChIA-PET data is recommended | |
LorDG [69] | 2016 | Yes | Java | Points | No | Distance | Optimization | Gradient ascent | Ensemble | Human: All chromosomes and whole genome (500kb –1Mb) | Hi–C contact matrix |
ISDHiC [90] | 2016 | No | C, C++, Python | Spheres | No | Distance | Probabilistic Modeling | MCMC sampling using Hamiltonian MC | Ensemble | Mouse: Chromosome X (50kb, 500kb) | Hi–C contact matrix |
Chrom3D [91] | 2017 | Yes | Perl | Spheres | No | Contact | Optimization | Monte Carlo-Optimization using the Metropolis–Hastings algorithm with simulated annealing | Ensemble | Human: Whole genome (TAD) | Hi–C contact matrix and LAD information |
miniMDS [92] | 2017 | Yes | Python | Points | No | Distance | Optimization | MDS approximation algorithms and Kabsch algorithm | Consensus | Human: Whole genome (10kp-100kb) | Hi–C contact matrix |
3DMax [70] | 2018 | Yes | Java, Matlab | Points | No | Distance | Optimization | Gradient ascent | Ensemble | Human: All chromosomes (1Mb) | Hi–C contact matrix |
GEM [93] | 2018 | Yes | Matlab | Polymer | No | Contact | Optimization | Adaptive gradient descent method | Ensemble | Human: Chromosome 13 and 14(1Mb), Chromosome 1 (250 kb: 130Mb-180Mb region ), Yeast : Chromosome 6 (10kb), | Hi–C contact matrix |
GEM–FISH [94] | 2018 | Yes | Matlab | Polymer | No | Contact | Optimization | Gradient descent | Consensus | Human: Chromosomes 20, 21, 22, and X (TAD) | Hi–C contact matrix and FISH data |
SIMBA3D [95] | 2018 | Yes | Python | Points | No | Probability | Probabilistic Modeling | BFGS mehtod with analytical gradient | Ensemble | Mouse: All chromosomes (100kb) | Hi–C contact matrix |
ShRec3D+ [96] | 2018 | No |  | Points | No | Distance | Optimization | Floyd-Warshall algorithm | Consensus | Human and Mouse: All chromosomes (1Mb) | Hi–C contact matrix |
EVR [97] | 2018 | Yes | C, Python | Points | No | Distance | Optimization | Error-Vector Resultant algorithm | Consensus | Bacteria: All chromosomes (10kb) | Hi–C contact matrix |
Hierarchical3DGenome [98] | 2019 | Yes | Java | Points | No | Distance | Optimization | Gradient ascent and hierarchical modeling | Ensemble | Human: All chromosomes (1kb - 5kb) | Hi–C contact matrix and File containing identified TADs |