Nanopore Dwell Time Analysis Permits Sequencing and Conformational Assignment of Pseudouridine in SARS-CoV-2

  1. Aaron M. Fleming
  2. Nicole J. Mathewson
  3. Shereen A. Howpay Manage
  4. Cynthia J. Burrows

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Abstract

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  1. Version published to 10.1021/acscentsci.1c00788
  2. ScreenIT

    SciScore for 10.1101/2021.05.10.443494: (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

    Software and Algorithms
    SentencesResources
    The data were first analyzed by MultiQC implemented in the Master of Pores tool,33 to yield read statistics similar to those described in the literature (Figures S2).
    MultiQC
    suggested: (MultiQC, RRID:SCR_014982)
    35 The bam files were indexed with Samtools and then visualized in IGV to obtain the base call frequency at the modification sites.36,37 The Eligos2 tool was used as described in the online manual on GitLab,27 and the Nanocompore and Tombo tools were used as described on their Github sites.
    Samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    29,38 The current and dwell time data were extracted, indexed to the reference, and analyzed using Nanopolish as described in the user manual.
    Nanopolish
    suggested: (Nanopolish, RRID:SCR_016157)
    39 The data were plotted and analyzed in either python, Origin, or Excel for visualization.
    python
    suggested: (IPython, RRID:SCR_001658)
    Excel
    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: We detected the following sentences addressing limitations in the study:
    At present, the signatures for all modifications are not known and studies are needed to address what the signals are, as well as what the biases and limitations are to the data analysis. In the present work, Ψ was synthetically incorporated by IVT in RNA at known locations in 18 different humanrelevant sequence contexts found in rRNA, mRNA, and tRNA (Figure S1). The Ψ sites were spaced >25 nt apart to study them one at a time as they pass from the helicase to the nanopore sensor, an overall distance that spans ~17 nt from the entry of the helicase to the exit of the k-mer sensing zone in the protein nanopore. Pseudouridine is base called by Guppy predominantly as U or C, consistent with other studies,26 and the present work found the ratio is dependent on the local sequence context (Figure 2). The base-calling error for Ψ was greater than U permitting detection of the modification (Figure 3); however, the base-calling errors were sequence context dependent, similar to the base-calling differences. Two extreme examples found in the data illustrate the challenges in using base-calling data for RNA modification sequence; in the 5’-ACXCA (X = U or Ψ) context, the U:C ratio is 7:88 with a base-calling error analysis giving an oddR value of 233, while the 5’-CAXCG context had a U:C ratio of 90:10 and an oddR value of 4, both when 100% Ψ is present at position X (Figures 2 and 3). In real samples, this approach will systematically favor observation of high error and high C calling ...

    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.

    Results from scite Reference Check: We found no unreliable references.

    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.

  3. Version published to 10.1101/2021.05.10.443494v1 on bioRxiv