# Comparing full variation profile analysis with the conventional consensus method in SARS-CoV-2 phylogeny

## TL;DR

This paper introduces a new method for analyzing SARS-CoV-2 mutations that improves accuracy by considering all nucleotides, not just the most common ones.

## Contribution

The novel genomic matrix approach captures low-frequency variants, improving mutation detection accuracy compared to consensus methods.

## Key findings

- The genomic matrix method showed a 20% average accuracy improvement over consensus methods in simulated datasets.
- Real-world data analysis revealed a 15% reduction in error margin using the genomic matrix approach.
- The method provides a more accurate representation of SARS-CoV-2 genomic diversity and evolution.

## Abstract

This study proposes a novel approach to studying severe acute respiratory syndrome coronavirus 2 virus mutations through sequencing data comparison. Traditional consensus-based methods, which focus on the most common nucleotide at each position, might overlook or obscure the presence of low-frequency variants. Our method, in contrast, retains all sequenced nucleotides at each position, forming a genomic matrix. Utilizing simulated short reads from genomes with specified mutations, we contrasted our genomic matrix approach with the consensus sequence method. Our matrix methodology, across multiple simulated datasets, accurately reflected the known mutations with an average accuracy improvement of 20% over the consensus method. In real-world tests using data from GISAID and NCBI-SRA, our approach demonstrated an increase in reliability by reducing the error margin by approximately 15%. The genomic matrix approach offers a more accurate representation of the viral genomic diversity, thereby providing superior insights into virus evolution and epidemiology.