Genome Profiling of SARS-CoV-2 in Indonesia, ASEAN and the Neighbouring East Asian Countries: Features, Challenges and Achievements

  1. Inswasti Cahyani
  2. Eko W. Putro
  3. Asep M. Ridwanuloh
  4. Satrio Wibowo
  5. Hariyatun Hariyatun
  6. Gita Syahputra
  7. Gilang Akbariani
  8. Ahmad R. Utomo
  9. Mohammad Ilyas
  10. Matthew Loose
  11. Wien Kusharyoto
  12. Susanti Susanti

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Abstract

Whole-genome sequencing (WGS) has played a significant role in understanding the epidemiology and biology of SARS-CoV-2 virus. Here, we investigate the use of SARS-CoV-2 WGS in Southeast and East Asian countries as a genomic surveillance during the COVID-19 pandemic. Nottingham–Indonesia Collaboration for Clinical Research and Training (NICCRAT) initiative has facilitated collaboration between the University of Nottingham and a team in the Research Center for Biotechnology, National Research and Innovation Agency (BRIN), to carry out a small number of SARS-CoV-2 WGS in Indonesia using Oxford Nanopore Technology (ONT). Analyses of SARS- CoV-2 genomes deposited on GISAID reveal the importance of clinical and demographic metadata collection and the importance of open access and data sharing. Lineage and phylogenetic analyses of two periods defined by the Delta variant outbreak reveal that: (1) B.1.466.2 variants were the most predominant in Indonesia before the Delta variant outbreak, having a unique spike gene mutation N439K at more than 98% frequency, (2) Delta variants AY.23 sub-lineage took over after June 2021, and (3) the highest rate of virus transmissions between Indonesia and other countries was through interactions with Singapore and Japan, two neighbouring countries with a high degree of access and travels to and from Indonesia.

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  1. Version published to 10.3390/v14040778
  2. Version published to 10.1101/2021.07.06.451270v3 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.07.06.451270: (What is this?)

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

    Table 1: Rigor

    EthicsField Sample Permit: The metadata included collection date, location, origin of samples, passage history, and sequencing technology.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Libraries were loaded on a MinION device and flow cell.
    MinION
    suggested: (MinION, RRID:SCR_017985)
    ONT’s MinKNOW software was used to run sequencing.
    MinKNOW
    suggested: None
    The Rampart software was applied to monitor coverage of each barcoded sample in real-time by running fast base-calling.
    Rampart
    suggested: (Rampart, RRID:SCR_016742)
    Up to 1 June 2021, there were 1830 sequences that were aligned and the 5’ and 3’ ends trimmed using MAFFT v.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    The tree was then visualized and annotated with FigTree where the nodes were ordered and rooted [75].
    FigTree
    suggested: (FigTree, RRID:SCR_008515)

    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.
    • 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.

    Results from scite Reference Check: We found no unreliable references.

    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.

  4. Version published to 10.1101/2021.07.06.451270v2 on bioRxiv
  5. Version published to 10.1101/2021.07.06.451270v1 on bioRxiv