Fedor M. Kazanov1, Evgenii V. Matveev2,3,4 , Gennady V. Ponomarev2,3, Dmitry N. Ivankov2 and Marat D. Kazanov2,3,4,5*
1 “Foxford” High School, Moscow, Russia
2 Skolkovo Institute of Science and Technology, Moscow, Russia
3 A.A. Kharkevich Institute for Information Transmission Problems, Moscow, Russia
4 Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
5 Sabanci University, Istanbul, Turkey
fedor.kazanov [at] gmail.com
Abstract
RNA, a molecule essential for numerous biological functions including genetic information storage, gene regulation, and catalytic activity. Its ability to form specific secondary and tertiary structures is crucial for its functionality. The field of RNA secondary structure analysis is advancing rapidly, owing to enhancements in both experimental and computational techniques. Recent research, especially focusing on RNA viruses like HIV, influenza, and SARS-CoV-2, demonstrates how viral RNA structures critically influence stages of the viral life cycle such as replication and immune evasion. This is exemplified by structures like the HIV-1 Rev response element and the SARS-CoV-2 frameshift stimulation element, which are pivotal in functional interactions essential for viral replication and protein synthesis.
However, existing RNA analysis bioinformatics tools have limitations, mostly focusing solely on nucleotide pairing and lacking the recognition of common patterns like hairpins, bulges, and pseudoknots. Here, we present a Python package specifically designed for analyzing RNA secondary structural elements in viral genomes. This tool facilitates the identification of common secondary structure patterns such as hairpin loops, internal loops, and pseudoknots, among others, and provides a framework for analysis of these elements to get insights into their properties. RNAsselem Python package is available at: http://github.com/KazanovLab/RNAsselem.
Keywords: RNA secondary structure, RNA viruses, hairpin, stem, loop.
Acknowledgement: This research was funded by Russian Science Foundation, grant number 22-14-00132.