RASCL: Rapid Assessment Of SARS-CoV-2 Clades Through Molecular Sequence Analysis

  1. Alexander G Lucaci
  2. Jordan D Zehr
  3. Stephen D Shank
  4. Dave Bouvier
  5. Han Mei
  6. Anton Nekrutenko
  7. Darren P Martin
  8. Sergei L Kosakovsky Pond

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Abstract

An important component of efforts to manage the ongoing COVID19 pandemic is the R apid A ssessment of how natural selection contributes to the emergence and proliferation of potentially dangerous S ARS-CoV-2 lineages and CL ades (RASCL). The RASCL pipeline enables continuous comparative phylogenetics-based selection analyses of rapidly growing clade-focused genome surveillance datasets, such as those produced following the initial detection of potentially dangerous variants. From such datasets RASCL automatically generates down-sampled codon alignments of individual genes/ORFs containing contextualizing background reference sequences, analyzes these with a battery of selection tests, and outputs results as both machine readable JSON files, and interactive notebook-based visualizations.

Availability

RASCL is available from a dedicated repository at https://github.com/veg/RASCL and as a Galaxy workflow https://usegalaxy.eu/u/hyphy/w/rascl . Existing clade/variant analysis results are available here: https://observablehq.com/@aglucaci/rascl .

Contact

Dr. Sergei L Kosakovsky Pond ( spond@temple.edu ).

Supplementary information

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    SciScore for 10.1101/2022.01.15.476448: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

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  2. Version published to 10.1101/2022.01.15.476448v1 on bioRxiv