Time Series Analysis of SARS-CoV-2 Genomes and Correlations among Highly Prevalent Mutations-Reference-Cited by-同舟云学术

Time Series Analysis of SARS-CoV-2 Genomes and Correlations among Highly Prevalent Mutations

Published:2022-10-26 Issue:5 Volume:10 Page:
ISSN:2165-0497
Container-title:Microbiology Spectrum
language:en
Short-container-title:Microbiol Spectr

Author:

Periwal Neha¹,Rathod Shravan B.²,Sarma Sankritya³,Johar Gundeep S.⁴,Jain Avantika¹⁵,Barnwal Ravi P.⁶,Srivastava Kinsukh R.⁷,Kaur Baljeet⁸,Arora Pooja³,Sood Vikas¹^ORCID

Affiliation:

1. Department of Biochemistry, SCLS, Jamia Hamdard, New Delhi, India

2. Department of Chemistry, Smt. S. M. Panchal Science College, Talod, Gujarat, India

3. Department of Zoology, Hansraj College, University of Delhi, New Delhi, India

4. Humber College, Toronto, Ontario, Canada

5. Delhi Institute of Pharmaceutical Sciences and Research, New Delhi, Delhi, India

6. Department of Biophysics, Panjab University, Chandigarh, India

7. Division of Medicinal and Process Chemistry, CDRI, Lucknow, Uttar Pradesh, India

8. Department of Computer Science, Hansraj College, University of Delhi, New Delhi, India

Abstract

We performed a meta-analysis on SARS-CoV-2 genomes categorized by collection month and identified several significant mutations. Pearson correlation analysis of these significant mutations identified 16 comutations having absolute correlation coefficients of >0.4 and a frequency of >30% in the genomes used in this study.

Funder

University Grants Commission

Publisher

American Society for Microbiology

Subject

Infectious Diseases,Cell Biology,Microbiology (medical),Genetics,General Immunology and Microbiology,Ecology,Physiology

Link

https://journals.asm.org/doi/pdf/10.1128/spectrum.01219-22

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