ESC: a comprehensive resource for SARS-CoV-2 immune escape variants

  1. Mercy Rophina
  2. Kavita Pandhare
  3. Afra Shamnath
  4. Mohamed Imran
  5. Bani Jolly
  6. Vinod Scaria

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Abstract

Ever since the breakout of COVID-19 disease, ceaseless genomic research to inspect the epidemiology and evolution of the pathogen has been undertaken globally. Large scale viral genome sequencing and analysis have uncovered the functional impact of numerous genetic variants in disease pathogenesis and transmission. Emerging evidence of mutations in spike protein domains escaping antibody neutralization is reported. We have built a database with precise collation of manually curated variants in SARS-CoV-2 from literature with potential escape mechanisms from a range of neutralizing antibodies. This comprehensive repository encompasses a total of 5258 variants accounting for 2068 unique variants tested against 230 antibodies, patient convalescent plasma and vaccine breakthrough events. This resource enables the user to gain access to an extensive annotation of SARS-CoV-2 escape variants which would contribute to exploring and understanding the underlying mechanisms of immune response against the pathogen. The resource is available at http://clingen.igib.res.in/esc/.

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  1. Version published to 10.1093/nar/gkab895
  2. Version published to 10.1101/2021.02.18.431922v2 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.02.18.431922: (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
    Literature reports with relevant information on antibody escape variants were retrieved from sources including PubMed,
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    LitCovid, Google Scholar and articles from preprint servers.
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    Variant Information and Annotations: The variant information and annotations were retrieved from annotation tables for individual features using ANNOVAR (Wang et al., 2010).
    ANNOVAR
    suggested: (ANNOVAR, RRID:SCR_012821)
    Database and Web Interface: The back-end of the web interface was implemented using Apache web server and MongoDB v3.4.10.
    MongoDB
    suggested: None

    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.

  4. Version published to 10.1101/2021.02.18.431922v1 on bioRxiv