A Comparison of Performance for Different SARS-Cov-2 Sequencing Protocols

  1. Juanjo Bermúdez

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

SARS-Cov-2 genome sequencing has been identified as a fundamental tool for fighting the COVID-19 pandemic. It is used, for example, for identifying new variants of the virus and for elaborating phylogenetic trees that help to trace the spread of the virus. In the present study we provide a comprehensive comparison between the quality of the assemblies obtained from different sequencing protocols. We demonstrate how some protocols actively promoted by different high-level administrations are inefficient and how less-used alternative protocols show a significant increased performance. This increase of performance could lead to cheaper sequencing protocols and therefore to a more convenient escalation of the sequencing efforts around the world.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.03.01.433428: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationFrom the results for these queries I randomly selected some runs and downloaded the data sets.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    In case the runs contained long reads Flye and Canu (9) was also applied.
    Canu
    suggested: (Canu, RRID:SCR_015880)
    SPAdes, rnaSPAdes and metaSPAdes have been demonstrated to be the best-performing open-source software for viral genome de-novo assembly in different previous studies.
    SPAdes
    suggested: (SPAdes, RRID:SCR_000131)
    rnaSPAdes
    suggested: (rnaSPAdes, RRID:SCR_016992)

    Results from OddPub: Thank you for sharing your 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.
    • No funding statement was detected.
    • 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 10.1101/2021.03.01.433428v1 on bioRxiv