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 the Third-Generation sequencing enable real-time and on-site data production, changing the research paradigms in environmental and medical sampling in virology. To take full advantage of large-scale data generated from long-read sequencing, an innovation in the downstream data analysis is necessary. Here, we discuss futuristic methods using machine learning approaches to analyze big genetic data. Machine learning combines pattern recognition and computational learning to perform predictive and exploratory data analysis. In particular, deep learning is a field of machine learning that is used to solve complex problems through artificial neural networks. Unlike other methods, features can be learned using neural networks entirely from data without manual specifications. We discuss the future of 21st-century virology by presenting futuristic approaches for virus studies using real-time data production and on-site data analysis with the 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 the downstream data analysis to meet the advances in sequencing technologies. We argue that artificial neural networks can innovate the downstream data analysis, as they can learn from big datasets without model assumptions or feature specifications, as opposed to the 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.