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Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency
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Alignment-free sequence comparison for virus genomes based on location correlation coefficient.

Lily He1, Siyang Sun2, Qianyue Zhang2

  • 1School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, PR China.

Infection, Genetics and Evolution : Journal of Molecular Epidemiology and Evolutionary Genetics in Infectious Diseases
|October 9, 2021
PubMed
Summary
This summary is machine-generated.

A new alignment-free method, the correlation coefficient feature vector (CCFV), analyzes viral evolution using DNA sequences. This approach accelerates evolutionary analysis and confirms bats as potential SARS-CoV-2 hosts.

Keywords:
Alignment-freeCorrelation measureDNA sequenceSARS-CoV-2

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Area of Science:

  • Genomics
  • Virology
  • Bioinformatics

Background:

  • Coronaviruses, particularly SARS-CoV-2, exhibit rapid mutation and widespread transmission, necessitating efficient evolutionary analysis for clinical diagnosis.
  • Traditional alignment-based DNA sequencing methods for evolutionary analysis are computationally intensive, consuming significant time and resources.
  • Alignment-free methods offer a promising alternative to overcome the limitations of traditional sequence alignment.

Purpose of the Study:

  • To introduce a novel alignment-free method, the Correlation Coefficient Feature Vector (CCFV), for rapid and efficient viral evolutionary analysis.
  • To demonstrate the CCFV method's effectiveness in analyzing the evolutionary relationships of human viruses, including SARS-CoV-2.
  • To assess the CCFV method's performance against traditional alignment-based approaches.

Main Methods:

  • Developed the Correlation Coefficient Feature Vector (CCFV) method, defining an L-step delay correlation measure for nucleotide positions in DNA sequences.
  • Generated a 16×L-dimensional numerical vector to characterize nucleotide distribution patterns.
  • Applied the CCFV method to evolutionary analysis of SARS-CoV-2, Dengue virus, Hepatitis B virus, and human rhinovirus.

Main Results:

  • The CCFV method achieved comparable or superior results to alignment-based methods in evolutionary analysis of common human viruses.
  • The CCFV method demonstrated improved speed in sequence comparison by avoiding the computational complexity of multiple sequence alignment.
  • Analysis using the CCFV method supported the hypothesis that bats are potential intermediate hosts for SARS-CoV-2.

Conclusions:

  • The CCFV method provides a computationally efficient and accurate alignment-free approach for viral evolutionary studies.
  • This novel method accelerates sequence comparison, aiding in rapid clinical diagnosis and pandemic response.
  • The findings reinforce the importance of studying viral evolution and host interactions, particularly for emerging infectious diseases like COVID-19.