Computer-aided analysis of proteins or nucleic acids seems like a matter of course nowadays; however, the history of Bioinformatics and Computational Biology is quite recent. The advent of high-throughput sequencing has led to the production of “big data”, which has also affected the field of virology. The collaboration between the communities of bioinformaticians and virologists already started a few decades ago and it was strongly enhanced by the recent SARS-CoV-2 pandemics. In this article, which is the first in a series on how bioinformatics can enhance virus research, we show that highly useful information is retrievable from selected general and dedicated databases. Indeed, an enormous amount of information—both in terms of nucleotide/protein sequences and their annotation—is deposited in the general databases of international organisations participating in the International Nucleotide Sequence Database Collaboration (INSDC). However, more and more virus-specific databases have been established and are progressively enriched with the contents and features reported in this article. Since viruses are intracellular obligate parasites, a special focus is given to host-pathogen protein-protein interaction databases. Finally, we illustrate several phylogenetic and phylodynamic tools, combining information on algorithms and features with practical information on how to use them and case studies that validate their usefulness. Databases and tools for functional inference will be covered in the next article of this series: Bioinformatics goes viral: II. Sequence-based and structure-based functional analyses for boosting virus research.

Driven by the common entropy formalism in statistical physics and information theory here we show that methods developed for the analysis of molecular conformational ensembles may be successfully used for classification. Entropy is estimated using the k-th nearest neighbour method combined with the Maximum Information Spanning Tree method to account for the mutual informations between variables. This formalism is used to classify new samples using their estimated cross-entropy with the training set. The method is tested using diverse datasets and the results obtained are comparable to those obtained by machine learning methods, or better when the number of samples is small. The methods described here have the advantage, compared to other classification methods, of directly linking variables and pairs of variables to the assigned class, a valuable feature when aiming at identifying causative relationships between features and classes (e.g. the relationship between genes and diseases).

Driven by the common entropy formalism in statistical physics and information theory, here we show that methods developed for the analysis of molecular configurational ensembles may be successfully used for classification. Entropy is estimated using the k-th nearest neighbor method combined with the Maximum Information Spanning Tree method to account for the mutual information between variables. This formalism is used to classify new samples using their estimated cross-entropy with the training set. The method is tested using the hand-written digits MNIST and the functional genomics CuMiDa datasets. The results obtained on these diverse datasets are comparable to those obtained by machine learning methods, or better when the number of samples is small. The methods described here have the advantage, compared to other classification methods, of directly linking variables and pairs of variables to the assigned class, a valuable feature when aiming at identifying causal relationships between features and classes (e.g., the relationship between genes and diseases).