Freely accessible ready to use global infrastructure for SARS-CoV-2 monitoring

  1. Wolfgang Maier
  2. Simon Bray
  3. Marius van den Beek
  4. Dave Bouvier
  5. Nathaniel Coraor
  6. Milad Miladi
  7. Babita Singh
  8. Jordi Rambla De Argila
  9. Dannon Baker
  10. Nathan Roach
  11. Simon Gladman
  12. Frederik Coppens
  13. Darren P Martin
  14. Andrew Lonie
  15. Björn Grüning
  16. Sergei L. Kosakovsky Pond
  17. Anton Nekrutenko

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Abstract

The COVID-19 pandemic is the first global health crisis to occur in the age of big genomic data.Although data generation capacity is well established and sufficiently standardized, analytical capacity is not. To establish analytical capacity it is necessary to pull together global computational resources and deliver the best open source tools and analysis workflows within a ready to use, universally accessible resource. Such a resource should not be controlled by a single research group, institution, or country. Instead it should be maintained by a community of users and developers who ensure that the system remains operational and populated with current tools. A community is also essential for facilitating the types of discourse needed to establish best analytical practices. Bringing together public computational research infrastructure from the USA, Europe, and Australia, we developed a distributed data analysis platform that accomplishes these goals. It is immediately accessible to anyone in the world and is designed for the analysis of rapidly growing collections of deep sequencing datasets. We demonstrate its utility by detecting allelic variants in high-quality existing SARS-CoV-2 sequencing datasets and by continuous reanalysis of COG-UK data. All workflows, data, and documentation is available at https://covid19.galaxyproject.org .

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  1. ScreenIT

    SciScore for 10.1101/2021.03.25.437046: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The two Illumina RNASeq workflows (#1 and #2 in Table 1) perform read mapping with bwa-mem and bowtie2, respectively, followed by sensitive allelic-variant (AV) calling across a wide range of AFs with lofreq (see AV calling section below).
    bowtie2
    suggested: (Bowtie 2, RRID:SCR_016368)
    All four workflows use SnpEff, specifically its 4.5covid19 version, for AV annotation.
    SnpEff
    suggested: (SnpEff, RRID:SCR_005191)
    The fifth workflow (Reporting) takes a table of AVs produced by any of the other four workflows and generates a list of AVs by Samples and by Variant.
    Variant
    suggested: (VARIANT, RRID:SCR_005194)
    SNVer is no longer actively maintained).
    SNVer
    suggested: (SNVer, RRID:SCR_002061)
    FreeBayes contains a mode specifically designed for finding sites with continuous AFs; Mutect2 features a so-called mitochondrial mode, and lofreq was specifically designed for microbial sequence analysis.
    FreeBayes
    suggested: (FreeBayes, RRID:SCR_010761)

    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.

  2. Version published to 10.1101/2021.03.25.437046 on bioRxiv