A 2D convolutional neural network for taxonomic classification applied to viruses in the phylum Cressdnaviricota

Taxonomy, defined as the classification of different objects/organisms into defined stable hierarchical categories (taxa), is fundamental for proper scientific communication. In virology, taxonomic assignments based on sequence alone are now possible and their use may contribute to a more precise and comprehensive framework. The current major challenge is to develop tools for the automated classification of the millions of putative new viruses discovered in metagenomic studies. Among the many tools that have been proposed, those applying machine learning (ML), mainly in the deep learning branch, stand out with highly accurate results. One ML tool recently released that uses k-mers, VirusTaxo, was the first one to be applied with success, 93% average accuracy, to all types of viruses. Nevertheless, there is a demand for new tools that are less computationally intensive. Viruses classified in the phylum Cressdnaviricota, with their small and compact genomes, are good subjects for testing these new tools. Here we tested the usage of 2D convolutional neural networks for the taxonomic classification of cressdnaviricots, also testing the effect of data imbalance and two augmentation techniques by benchmarking against VirusTaxo. We were able to get perfect classification during k-fold test evaluations for balanced taxas, and more than 98% accuracy in the final pipeline tested for imbalanced datasets. The mixture of augmentation on more imbalanced groups and no augmentation for more balanced ones achieved the best score in the final test. These results indicate that these architectures can classify DNA sequences with high precision.