Aleksandr Koshkarov
Department of Computer Science, University of Sherbrooke, Sherbrooke, QC J1K2R1, Canada
Center of Artificial Intelligence, Astrakhan State University, Astrakhan, 414056, Russia
Contributed equally

Wanlin Li
Department of Computer Science, University of Sherbrooke, Sherbrooke, QC J1K2R1, Canada
Contributed equally

My-Linh Luu
Department of Computer Science, University of Quebec at Montreal, Montreal, QC, Canada

Nadia Tahiri
Department of Computer Science, University of Sherbrooke, Sherbrooke, QC J1K2R1, Canada

Abstract

Due to the fact that the SARS-CoV-2 pandemic reaches its peak, researchers around the globe are combining efforts to investigate the genetics of different variants to better deal with its distribution. This paper discusses phylogeographic approaches to examine how patterns of divergence within SARS-CoV-2 coincide with geographic features, such as climatic features. First, we propose a python-based bioinformatic pipeline called aPhylogeo for phylogeographic analysis written in Python 3 that help researchers better understand the distribution of the virus in specific regions via a configuration file, and then run all the analysis operations in a single run. In particular, the aPhylogeo tool determines which parts of the genetic sequence undergo a high mutation rate depending on geographic conditions, using a sliding window that moves along the genetic sequence alignment in user-defined steps and a window size. As a Python-based cross-platform program, aPhylogeo works on Windows®, MacOS X® and GNU/Linux. The implementation of this pipeline is publicly available on GitHub (https://github.com/tahiri-lab/aPhylogeo). Second, we present an example of analysis of our new aPhylogeo tool on real data (SARS-CoV-2) to understand the occurrence of different variants.

Keywords

Phylogeography, SARS-CoV-2, Bioinformatics, Genetic, Climatic Condition

DOI

10.25080/majora-212e5952-018

Bibtex entry

Full text PDF

Copyright © 2022 Aleksandr Koshkarov et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.