This paper tackles the information of 133 RNA viruses available in public databases under the light of several mathematical and computational tools. First, the formal concepts of distance metrics, Kolmogorov complexity and Shannon information are recalled. Second, the computational tools available presently for tackling and visualizing patterns embedded in datasets, such as the hierarchical clustering and the multidimensional scaling, are discussed. The synergies of the common application of the mathematical and computational resources are then used for exploring the RNA data, cross-evaluating the normalized compression distance, entropy and Jensen–Shannon divergence, versus representations in two and three dimensions. The results of these different perspectives give extra light in what concerns the relations between the distinct RNA viruses.

本文根据几种数学和计算工具处理了公共数据库中可用的 133 种 RNA 病毒的信息。首先，回顾距离度量、Kolmogorov 复杂度和香农信息的正式概念。其次，讨论了目前可用于处理和可视化嵌入数据集中的模式的计算工具，例如层次聚类和多维缩放。然后将数学和计算资源的共同应用的协同作用用于探索 RNA 数据，交叉评估归一化压缩距离、熵和 Jensen-Shannon 散度，以及二维和三维的表示。这些不同观点的结果为不同RNA病毒之间的关系提供了额外的线索。