Publications

Authors:

Formentin, Marco; Chignola, Roberto; Favretti, Marco

Title:

Optimal entropic properties of SARS-CoV-2 RNA sequences

Year:

2024

Type of item:

Articolo in Rivista

Tipologia ANVUR:

Articolo su rivista

Language:

Inglese

Format:

Elettronico

Referee:

Sì

Name of journal:

ROYAL SOCIETY OPEN SCIENCE

ISSN of journal:

2054-5703

N° Volume:

11

Page numbers:

1-12

Keyword:

Shannon entropy; mutual information; CT mutation bias

Short description of contents:

The reaction of the scientific community against the COVID-19 pandemic has generated a huge (approx. 106 entries) dataset of genome sequences collected worldwide and spanning a relatively short time window. These unprecedented conditions together with the certain identification of the reference viral genome sequence allow for an original statistical study of mutations in the virus genome. In this paper, we compute the Shannon entropy of every sequence in the dataset as well as the relative entropy and the mutual information between the reference sequence and the mutated ones. These functions, originally developed in information theory, measure the information content of a sequence and allows us to study the random character of mutation mechanism in terms of its entropy and information gain or loss. We show that this approach allows us to set in new format known features of the SARS-CoV-2 mutation mechanism like the CT bias, but also to discover new optimal entropic properties of the mutation process in the sense that the virus mutation mechanism track closely theoretically computable lower bounds for the entropy decrease and the information transfer.

Web page:

https://royalsocietypublishing.org/doi/10.1098/rsos.231369

Product ID:

137431

Handle IRIS:

11562/1118209

Last Modified:

November 6, 2024

Bibliographic citation:

Formentin, Marco; Chignola, Roberto; Favretti, Marco, Optimal entropic properties of SARS-CoV-2 RNA sequences «ROYAL SOCIETY OPEN SCIENCE» , vol. 11 , 2024 , pp. 1-12

Consulta la scheda completa presente nel repository istituzionale della Ricerca di Ateneo