The UCSC SARS-CoV-2 Genome Browser

  1. Jason D. Fernandes
  2. Angie S. Hinrichs
  3. Hiram Clawson
  4. Jairo Navarro Gonzalez
  5. Brian T. Lee
  6. Luis R. Nassar
  7. Brian J. Raney
  8. Kate R. Rosenbloom
  9. Santrupti Nerli
  10. Arjun Rao
  11. Daniel Schmelter
  12. Ann S. Zweig
  13. Todd M. Lowe
  14. Manuel Ares
  15. Russ Corbet-Detig
  16. W. James Kent
  17. David Haussler
  18. Maximilian Haeussler

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Abstract

Background

Researchers are generating molecular data pertaining to the SARS-CoV-2 RNA genome and its proteins at an unprecedented rate during the COVID-19 pandemic. As a result, there is a critical need for rapid and continuously updated access to the latest molecular data in a format in which all data can be quickly cross-referenced and compared. We adapted our genome browser visualization tool to the viral genome for this purpose. Molecular data, curated from published studies or from database submissions, are mapped to the viral genome and grouped together into “annotation tracks” where they can be visualized along the linear map of the viral genome sequence and programmatically downloaded in standard format for analysis.

Results

The UCSC Genome Browser for SARS-CoV-2 ( https://genome.ucsc.edu/covid19.html ) provides continuously updated access to the mutations in the many thousands of SARS-CoV-2 genomes deposited in GISAID and the international nucleotide sequencing databases, displayed alongside phylogenetic trees. These data are augmented with alignments of bat, pangolin, and other animal and human coronavirus genomes, including per-base evolutionary rate analysis. All available annotations are cross-referenced on the virus genome, including those from major databases (PDB, RFAM, IEDB, UniProt) as well as up-to-date individual results from preprints. Annotated data include predicted and validated immune epitopes, promising antibodies, RT-PCR and sequencing primers, CRISPR guides (from research, diagnostics, vaccines, and therapies), and points of interaction between human and viral genes. As a community resource, any user can add manual annotations which are quality checked and shared publicly on the browser the next day.

Conclusions

We invite all investigators to contribute additional data and annotations to this resource to accelerate research and development activities globally. Contact us at genome-www@soe.ucsc.edu with data suggestions or requests for support for adding data. Rapid sharing of data will accelerate SARS-CoV-2 research, especially when researchers take time to integrate their data with those from other labs on a widely-used community browser platform with standardized machine-readable data formats, such as the SARS-CoV-2 Genome Browser.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.04.075945: (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

    No key resources detected.

    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 10.1101/2020.05.04.075945 on bioRxiv