periscope: sub-genomic RNA identification in SARS-CoV-2 Genomic Sequencing Data

  1. Matthew D Parker
  2. Benjamin B Lindsey
  3. Shay Leary
  4. Silvana Gaudieri
  5. Abha Chopra
  6. Matthew Wyles
  7. Adrienn Angyal
  8. Luke R Green
  9. Paul Parsons
  10. Rachel M Tucker
  11. Rebecca Brown
  12. Danielle Groves
  13. Katie Johnson
  14. Laura Carrilero
  15. Joe Heffer
  16. David G Partridge
  17. Cariad Evans
  18. Mohammad Raza
  19. Alexander J Keeley
  20. Nikki Smith
  21. Ana Da Silva Filipe
  22. James G Shepherd
  23. Chris Davis
  24. Sahan Bennett
  25. Alain Kohl
  26. Elihu Aranday-Cortes
  27. Lily Tong
  28. Jenna Nichols
  29. Emma C Thomson
  30. The COVID-19 Genomics UK (COG-UK) consortium
  31. Dennis Wang
  32. Simon Mallal
  33. Thushan I de Silva

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Abstract

We have developed periscope, a tool for the detection and quantification of sub-genomic RNA (sgRNA) in SARS-CoV-2 genomic sequence data. The translation of the SARS-CoV-2 RNA genome for most open reading frames (ORFs) occurs via RNA intermediates termed “sub-genomic RNAs”. sgRNAs are produced through discontinuous transcription which relies on homology between transcription regulatory sequences (TRS-B) upstream of the ORF start codons and that of the TRS-L which is located in the 5’ UTR. TRS-L is immediately preceded by a leader sequence. This leader sequence is therefore found at the 5’ end of all sgRNA. We applied periscope to 1,155 SARS-CoV-2 genomes from Sheffield, UK and validated our findings using orthogonal datasets and in vitro cell systems. Using a simple local alignment to detect reads which contain the leader sequence we were able to identify and quantify reads arising from canonical and non-canonical sgRNA. We were able to detect all canonical sgRNAs at expected abundances, with the exception of ORF10. A number of recurrent non-canonical sgRNAs are detected. We show that the results are reproducible using technical replicates and determine the optimum number of reads for sgRNA analysis. In VeroE6 ACE2+/− cell lines, periscope can detect the changes in the kinetics of sgRNA in orthogonal sequencing datasets. Finally, variants found in genomic RNA are transmitted to sgRNAs with high fidelity in most cases. This tool can be applied to all sequenced COVID-19 samples worldwide to provide comprehensive analysis of SARS-CoV-2 sgRNA.

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  1. Version published to 10.1101/2020.07.01.181867v2 on bioRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.07.01.181867: (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.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    One VeroE6 cell line was manipulated to overexpress the angiotensin-converting enzyme 2 (Ace2) receptor.
    VeroE6
    suggested: JCRB Cat# JCRB1819, RRID:CVCL_YQ49)
    Software and Algorithms
    SentencesResources
    Analysis and Figure Generation: Further analysis was completed in R 3.5.2(Schulte et al. 2012) using Rstudio 1.1.442(Racine 2012), in general data was processed using dplyr (v0.8.3), figures were generated using ggplot2 (v3.3.1), both part of the tidyverse(Wickham et al. 2019) family of packages (v1.2.1).
    Rstudio
    suggested: (RStudio, RRID:SCR_000432)
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    Reads were visualised in IGV(Robinson et al. 2011) and annotated with Adobe Illustrator.
    Adobe Illustrator
    suggested: (Adobe Illustrator, RRID:SCR_010279)
    Data Cleaning: Data was uploaded to ENA after removing human reads with dehumaizer (https://github.com/SamStudio8/dehumanizer) Periscope Requirements: Periscope is a wrapper for a snakemake(Köster and Rahmann 2012) workflow with a package written in python to implement read filtering and classification, and is provided with a conda environment definition.
    python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code and data.

    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

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  3. Version published to 10.1101/2020.07.01.181867v1 on bioRxiv