Integrated genomic view of SARS-CoV-2 in India

  1. Pramod Kumar
  2. Rajesh Pandey
  3. Pooja Sharma
  4. Mahesh S Dhar
  5. A Vivekanand
  6. Bharathram Uppili
  7. Himanshu Vashisht
  8. Saruchi Wadhwa
  9. Nishu Tyagi
  10. Saman Fatihi
  11. Uma Sharma
  12. Priyanka Singh
  13. Hemlata Lall
  14. Meena Datta
  15. Poonam Gupta
  16. Nidhi Saini
  17. Aarti Tewari
  18. Bibhash Nandi
  19. Dhirendra Kumar
  20. Satyabrata Bag
  21. Deepanshi
  22. Surabhi Rathore
  23. Nidhi Jatana
  24. Varun Jaiswal
  25. Hema Gogia
  26. Preeti Madan
  27. Simrita Singh
  28. Prateek Singh
  29. Debasis Dash
  30. Manju Bala
  31. Sandhya Kabra
  32. Sujeet Singh
  33. Mitali Mukerji
  34. Lipi Thukral
  35. Mohammed Faruq
  36. Anurag Agrawal
  37. Partha Rakshit

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Abstract

India first detected SARS-CoV-2, causal agent of COVID-19 in late January-2020, imported from Wuhan, China. March-2020 onwards; importation of cases from rest of the countries followed by seeding of local transmission triggered further outbreaks in India. We used ARTIC protocol based tiling amplicon sequencing of SARS-CoV-2 (n=104) from different states of India using a combination of MinION and MinIT from Oxford Nanopore Technology to understand introduction and local transmission. The analyses revealed multiple introductions of SARS-CoV-2 from Europe and Asia following local transmission. The most prevalent genomes with patterns of variance (confined in a cluster) remain unclassified, here, proposed as A4-clade based on its divergence within A-cluster. The viral haplotypes may link their persistence to geo-climatic conditions and host response. Despite the effectiveness of non-therapeutic interventions in India, multipronged strategies including molecular surveillance based on real-time viral genomic data is of paramount importance for a timely management of the pandemic.

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  1. ScreenIT

    SciScore for 10.1101/2020.06.04.128751: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Samples from each group were selected and further processed for WGS of the SARS-CoV-2.
    WGS
    suggested: None
    Illumina’s MiSeq platform was used for sequencing.
    MiSeq
    suggested: (A5-miseq, RRID:SCR_012148)
    Phylogeny and network analysis: The fasta sequences were aligned using MAFFT considering the MN90894.3 version as the reference sequence.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    FIGTREE was used for the graphical visualisation of Phylogenetic analysis (http://tree.bio.ed.ac.uk/software/figtree).
    FIGTREE
    suggested: (FigTree, RRID:SCR_008515)
    Pheatmap and complex heatmap packages from R were used to plot the heatmaps.
    Pheatmap
    suggested: (pheatmap, RRID:SCR_016418)

    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: Please consider improving the rainbow (“jet”) colormap(s) used on page 23. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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 10.1101/2020.06.04.128751 on bioRxiv