CoV2ID: Detection and Therapeutics Oligo Database for SARS-CoV-2

  1. João Carneiro
  2. Catarina Gomes
  3. Cátia Couto
  4. Filipe Pereira

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Abstract

The ability to detect the SARS-CoV-2 in a widespread epidemic is crucial for screening of carriers and for the success of quarantine efforts. Methods based on real-time reverse transcription polymerase chain reaction (RT-qPCR) and sequencing are being used for virus detection and characterization. However, RNA viruses are known for their high genetic diversity which poses a challenge for the design of efficient nucleic acid-based assays. The first SARS-CoV-2 genomic sequences already showed novel mutations, which may affect the efficiency of available screening tests leading to false-negative diagnosis or inefficient therapeutics. Here we describe the CoV2ID ( http://covid.portugene.com/ ), a free database built to facilitate the evaluation of molecular methods for detection of SARS-CoV-2 and treatment of COVID-19. The database evaluates the available oligonucleotide sequences (PCR primers, RT-qPCR probes, etc.) considering the genetic diversity of the virus. Updated sequences alignments are used to constantly verify the theoretical efficiency of available testing methods. Detailed information on available detection protocols are also available to help laboratories implementing SARS-CoV-2 testing.

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  1. Version published to 10.1101/2020.04.19.048991v2 on bioRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.04.19.048991: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Python and Perl in-house algorithms were written and used to perform identity and pairwise calculations. 2.2. Oligonucleotides: The oligonucleotides were retrieved from peer reviewed publications [e.g.,7–14] and protocols provided by the World Health Organization (WHO)
    Python
    suggested: (IPython, RRID:SCR_001658)
    The first release of the database includes three multiple sequence alignments: The genomes from the NCBI were aligned using an optimized version of MUSCLE running at the NCBI Variation Resource.
    MUSCLE
    suggested: (MUSCLE, RRID:SCR_011812)
    The genomes from GISAID were aligned using the default parameters of the MAFFT version 7.17 The sequences can be visualized, edited and exported using the NCBI (https://www.ncbi.nlm.nih.gov/tools/sviewer/) and the Wasabi (http://wasabi2.biocenter.helsinki.fi/) tools.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)

    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

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  3. Version published to 10.1101/2020.04.19.048991v1 on bioRxiv