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Updated: Nov 14, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
Analysis and comparison of genetic variants and mutations of the novel coronavirus SARS-CoV-2
Zaid Almubaid1, Hisham Al-Mubaid2
1University of Texas, Austin, TX, USA.
Abstract:
We present an analysis and comparison study of genetic variants and mutations of about 1200 genomes of SARS-CoV-2 virus sampled across the first seven months of 2020. The study includes 12 sets of about 100 genomes each collected between January and September. We analyzed the mutations, mutation frequency and count trends over time, and genomes trends over time from January through September. We show that certain mutations in the SARS-CoV-2 genome are not occurring randomly as it has been commonly believed. This finding is in agreement with other recently published research in this domain. Therefore, this validates other findings in this direction. This study includes approximately 1000 genomes and was able to identify over 35 different mutations most of which are common to almost all genomes groups. Some mutations' ratios (frequency percentage) fluctuate over time to adapt the virus to various environmental factors, climate, and populations. One of the interesting findings in this paper is that the coding region, at the nucleotide level for NSP13 protein is relatively conserved compared with other protein regions in the ORF1ab gene which makes this protein a good candidate for developing drug targets and treatment for the COVID-19 disease. Although this outcome was already reported by other researchers, we corroborated their result with our work in a different approach and another experimental setting with almost one thousand complete genome sequences. We presented and discussed all these results and findings with tables of results and illustrating figures.
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