A comprehensive profile of genomic variations in the SARS-CoV-2 isolates from the state of Telangana, India

  1. Asmita Gupta
  2. Radhakrishnan Sabarinathan
  3. Pratyusha Bala
  4. Vinay Donipadi
  5. Divya Vashisht
  6. Madhumohan Rao Katika
  7. Manohar Kandakatla
  8. Debashis Mitra
  9. Ashwin Dalal
  10. Murali Dharan Bashyam

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Abstract

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing COVID-19 has rapidly turned into a pandemic, infecting millions and causing 1 157 509 (as of 27 October 2020) deaths across the globe. In addition to studying the mode of transmission and evasion of host immune system, analysing the viral mutational landscape constitutes an area under active research. The latter is expected to impart knowledge on the emergence of different clades, subclades, viral protein functions and protein–protein and protein–RNA interactions during replication/transcription cycle of virus and response to host immune checkpoints. In this study, we have attempted to bring forth the viral genomic variants defining the major clade(s) as identified from samples collected from the state of Telangana, India. We further report a comprehensive draft of all genomic variations (including unique mutations) present in SARS-CoV-2 strain in the state of Telangana. Our results reveal the presence of two mutually exclusive subgroups defined by specific variants within the dominant clade present in the population. This work attempts to bridge the critical gap regarding the genomic landscape and associate mutations in SARS-CoV-2 from a highly infected southern region of India, which was lacking to date.

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  1. ScreenIT

    SciScore for 10.1101/2020.08.24.20180810: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIACUC: The work was initiated following approvals from the Institutional Bioethics committee and Biosafety committee.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Mutation analysis: All raw fastq files from Illumina were checked for overall sequencing quality, presence of adapters and bad quality reads using FastQC and Fastp4.
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    The adapter sequences were trimmed using a wrapper script for Cutadapt5 tool, called Trim Galore.
    Trim Galore
    suggested: (Trim Galore, RRID:SCR_011847)
    Post alignment, the reads were filtered, sorted and indexed using samtools, and any primer sequences were masked using iVar8.
    samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)

    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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • 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 10.1099/jgv.0.001562 on Access Microbiology
  3. Version published to 10.1101/2020.08.24.20180810v1 on medRxiv