  The aim of the study is to analyze viruses using parameters obtained from
distributions of nucleotide sequences in the viral RNA. Seeking for the input
data homogeneity, we analyze single-stranded RNA viruses only. Two approaches
are used to obtain the nucleotide sequences; In the first one, chunks of equal
length (four nucleotides) are considered. In the second approach, the whole RNA
genome is divided into parts by adenine or the most frequent nucleotide as a
"space". Rank--frequency distributions are studied in both cases. Within the
first approach, the P\'olya and the negative hypergeometric distribution yield
the best fit. For the distributions obtained within the second approach, we
have calculated a set of parameters, including entropy, mean sequence length,
and its dispersion. The calculated parameters became the basis for the
classification of viruses. We observed that proximity of viruses on planes
spanned on various pairs of parameters corresponds to related species. In
certain cases, such a proximity is observed for unrelated species as well
calling thus for the expansion of the set of parameters used in the
classification. We also observed that the fourth most frequent nucleotide
sequences obtained within the second approach are of different nature in case
of human coronaviruses (different nucleotides for MERS, SARS-CoV, and
SARS-CoV-2 versus identical nucleotides for four other coronaviruses). We
expect that our findings will be useful as a supplementary tool in the
classification of diseases caused by RNA viruses with respect to severity and
contagiousness.
