Unique mutational changes in SARS-CoV-2 genome: A case study for the largest state of India

  1. Priti Prasad
  2. Shantanu Prakash
  3. Kishan Sahu
  4. Babita Singh
  5. Suruchi Shukla
  6. Hricha Mishra
  7. Danish Nasar Khan
  8. Om Prakash
  9. MLB Bhatt
  10. SK Barik
  11. Mehar H. Asif
  12. Samir V. Sawant
  13. Amita Jain
  14. Sumit Kr. Bag

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

COVID-19 is a global pandemic causing more than 8 million deaths till mid-August, 2020. In India, more than 3 million confirmed cases have been reported although with relatively low death rate of 1.8%. In this study, we sequenced 47 genomes of SARS-CoV-2 from the patients of 13 districts of Uttar Pradesh (UP), the largest state of India using third-generation sequencing technique. The phylogenetic clustering revealed that no UP sample was aligned with the previously defined USA clade, where the mortality was high. We identified 56 distinct SNP variations in the genomes of UP resulting in a unique mutation rate of 1.19% per sequence, which is greater than the value 0.88% obtained for the rest of India. The relatively less death rate in UP indicates that the mutation in the virus is deleterious. Further investigation is required with larger sample size to determine the degree of virulence vis-a-vis SNP variation.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.08.24.265827: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    For PromethION sequencing, libraries were prepared using the ligation sequencing kit (SQK-LSK109) and Native barcoding (EXP-NBD 104 and EXP-NBD 114) of 24 samples in a single flow cell.
    PromethION
    suggested: (PromethION, RRID:SCR_017987)
    2.5 Divergence estimates of the newly assembled genome: Divergence estimation of the newly assembled genome and the most recent common ancestor (tMRCA) were examined using the BEAST v1.10.4 program [26].
    BEAST
    suggested: (BEAST, RRID:SCR_010228)
    To identify the SNP variants among all the processed (downloaded and assembled) sequences and to ensure the uniqueness of variants in our assembled data, we aligned each downloaded sequences separately to the reference genome (MN908947.3) by use of mafft aligner [23].
    mafft
    suggested: (MAFFT, RRID:SCR_011811)
    The resulting Variant Call Format (VCF) file was annotated further to find out the position of the mutation that affects the SARS-CoV-2 virus genome though snpEff program [29].
    snpEff
    suggested: (SnpEff, RRID:SCR_005191)
    Functional validation of the emerging new mutations in UP sequences was undertaken through the Sorting Intolerant from Tolerant (SIFT) [30].
    SIFT
    suggested: (SIFT, RRID:SCR_012813)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.08.24.265827 on bioRxiv