The Global Landscape of SARS-CoV-2 Genomes, Variants, and Haplotypes in 2019nCoVR

  1. Shuhui Song
  2. Lina Ma
  3. Dong Zou
  4. Dongmei Tian
  5. Cuiping Li
  6. Junwei Zhu
  7. Meili Chen
  8. Anke Wang
  9. Yingke Ma
  10. Mengwei Li
  11. Xufei Teng
  12. Ying Cui
  13. Guangya Duan
  14. Mochen Zhang
  15. Tong Jin
  16. Chengmin Shi
  17. Zhenglin Du
  18. Yadong Zhang
  19. Chuandong Liu
  20. Rujiao Li
  21. Jingyao Zeng
  22. Lili Hao
  23. Shuai Jiang
  24. Hua Chen
  25. Dali Han
  26. Jingfa Xiao
  27. Zhang Zhang
  28. Wenming Zhao
  29. Yongbiao Xue
  30. Yiming Bao

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Abstract

On January 22, 2020, China National Center for Bioinformation (CNCB) released the 2019 Novel Coronavirus Resource (2019nCoVR), an open-access information resource for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). 2019nCoVR features a comprehensive integration of sequence and clinical information for all publicly available SARS-CoV-2 isolates, which are manually curated with value-added annotations and quality evaluated by an automated in-house pipeline. Of particular note, 2019nCoVR offers systematic analyses to generate a dynamic landscape of SARS-CoV-2 genomic variations at a global scale. It provides all identified variants and their detailed statistics for each virus isolate, and congregates the quality score, functional annotation, and population frequency for each variant. Spatiotemporal change for each variant can be visualized and historical viral haplotype network maps for the course of the outbreak are also generated based on all complete and high-quality genomes available. Moreover, 2019nCoVR provides a full collection of SARS-CoV-2 relevant literature on the coronavirus disease 2019 (COVID-19), including published papers from PubMed as well as preprints from services such as bioRxiv and medRxiv through Europe PMC. Furthermore, by linking with relevant databases in CNCB, 2019nCoVR offers data submission services for raw sequence reads and assembled genomes, and data sharing with NCBI. Collectively, SARS-CoV-2 is updated daily to collect the latest information on genome sequences, variants, haplotypes, and literature for a timely reflection, making 2019nCoVR a valuable resource for the global research community. 2019nCoVR is accessible at https://bigd.big.ac.cn/ncov/.

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  1. Version published to 10.1016/j.gpb.2020.09.001
  2. Version published to 10.1101/2020.08.30.273235v2 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.08.30.273235: (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
    Data collection and integration: All genome sequences as well as their related metadata were integrated from SARS-CoV-2 resources worldwide, including NCBI [5], GISAID [15], CNCB-NGDC [16], NMDC [17] and CNGB [18].
    NCBI
    suggested: (NCBI, RRID:SCR_006472)
    Genome sequence alignment was performed with Muscle (3.8.31) [19] by comparing against the earliest released SARS-CoV-2 genome (MN908947.3).
    Muscle
    suggested: (MUSCLE, RRID:SCR_011812)
    The effect of variants was determined using VEP (ENSEMBL Variant Effect Predictor) [20].
    ENSEMBL Variant Effect Predictor
    suggested: None
    Variant
    suggested: (VARIANT, RRID:SCR_005194)

    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.

    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 10.1101/2020.08.30.273235v1 on bioRxiv