  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.
