Futuristic Methods in Virus Genome Evolution Using the Third-Generation DNA Sequencing and Artificial Neural Networks

Abstract

The Third-Generation in DNA sequencing has emerged in the last few years, using new technologies that allow the production of long-read sequences. Applications of Third-Generation sequencing enable real-time data production, changing the research paradigms in environmental, and facilitating medical sampling in virology. To take full advantage of the large-scale data generated from long-read sequencing, an innovation in downstream data analysis is necessary. Here, we discuss futuristic methods using machine learning approaches to analyze big genetic data. We discuss the future of twenty-first-century virology by presenting advanced approaches for virus studies using real-time data production and on-site data analysis with Third-Generation Sequencing and machine learning methods. We first introduce the basic concepts in conventional statistical models and methods in virology, building gradually into the necessity of innovating downstream data analysis to meet the advances in sequencing technologies. We argue that artificial neural networks can innovate downstream data analysis, as they can learn from big datasets without model assumptions nor feature specifications, as opposed to current data analysis in bioinformatics. Furthermore, we discuss how futuristic methods using artificial neural networks, combined with long-read sequences can revolutionize virus studies, using specific examples in supervised and unsupervised settings.