Genomic determinants in pathogenicity of SARS-CoV-2 versa common cold coronaviruses

Determination of the different short oligonucleotide features in the full genome of fatal and mild coronavirus strains can show the researchers how these viruses evolved and became virulent strains. To this aim, at first, in the full genome of all coronavirus strains included in this study, the observed and expected frequency of dinucleotide to hexanucleotide was obtained using Markov method. Then odds ratio (observed/expected abundances) of short oligonucleotide was computed and considered as the raw data (features). Finally, ten distinct weighting algorithms approaches (Information Gain, Information Gain Ratio, Rule, Deviation, Chi Squared, Gini Index, Uncertainty, Relief, Support Vector Machine (SVM), and PCA) was employed on the features to identify oligonucleotide distribution differences across the full genome of SARS-related viruses compared to common cold coronaviruses. Totally among 5440 features (16 dinucleotides, 64 trinucleotides, 256 tetra nucleotides, 1024 penta-nucleotides, and 4096 Hexa-nucleotides), CC, CCA, CCAC, ACCAC, and CACCAC motifs were selected by 80 -90% of all weighting algorithms models to distinguish virulent strains from mild coronaviruses. These remarkable oligonucleotides might point toward the existence of some particular RNA elements that might be involved in viral virulence and thus can be targeted for viral treatment in the future.