Table 2.

Data science tools and techniques for SARS-CoV-2 data analysis

Task	Data type	Data science models	Available tools
Phylogeny/ alignment	Nucleotide/protein sequence	UPGMA, WPGMA, neighbour-joining, maximum likelihood, Fitch–Margoliash method, maximum parsimony, Bayesian inference	ClustalW, Clustal, MAFFT, MUSCLE, T-Coffee https://www.ebi.ac.uk/Tools/  https://www.genome.jp/tools-bin/clustalw DNAMAN https://www.lynnon.com/dnaman.html
Structure prediction	Protein sequence	Deep neural network (NeBcon, ResPRE, ResTriplet and TripletRes), QSQE, supervised machine learning (SVM), multiple regression	SWISS-MODEL [79], PyMOL [80], I-Tasser [81], COMPOSER [78]
SARS-CoV-2 predictor	Nucleotide sequence	Conventional models (Naïve Bayes, K-nearest neighbors, artificial neural networks, decision tree and support vector machine), deep models CNN, Bi-path CNN (BiPathCNN)	COVID-Predictor [132]
Protein interactions	Protein sequence, PPI networks, protein structure	Graph analysis	Cytoscape https://apps.cytoscape.org/
Chest imaging analysis	Chest x-ray or CT image	Deep learning models (VGG19), Mobile Net, Inception, Xception and Inception ResNet (v2,18,23,50), GAN, Dice similarity coefficient (DSC)	TrainingData.io https://www.trainingdata.io/
Epidemic trend analysis	Experimental and observational	LSTM statistical models (SIR, Bayesian imputation, linear and polynomial regression)	Worldometers-coronavirus https://www.worldometers.info/coronavirus/ WHO-COVID19-report https://www.who.int/emergencies/diseases/novel-coronavirus-2019 COVID-19 Projections https://covid19-projections.com/
Drug interaction and repurposing	Protein sequence, drug molecules, protein structure	Graph analysis, graphical convolution network	DeepDR [133], kGCN [134], DeepChem [135], D3Targets-2019-nCoV [136], CoVex [137]