AutoVEM: An automated tool to real-time monitor epidemic trends and key mutations in SARS-CoV-2 evolution

  1. Binbin Xi
  2. Dawei Jiang
  3. Shuhua Li
  4. Jerome R. Lon
  5. Yunmeng Bai
  6. Shudai Lin
  7. Meiling Hu
  8. Yuhuan Meng
  9. Yimo Qu
  10. Yuting Huang
  11. Wei Liu
  12. Lizhen Huang
  13. Hongli Du

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

No abstract available

Article activity feed

  1. Version published to 10.1016/j.csbj.2021.04.002
  2. ScreenIT

    SciScore for 10.1101/2020.12.24.424271: (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.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    AutoVEM is written in python language (python 3.8.6) and runs on Linux machines with centos.
    python
    suggested: (IPython, RRID:SCR_001658)
    And bowtie2 (17), samtools (18), bcftools (19), vcftools (20) and haploview (21) were applied in this automated tool.
    samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    Workflow of AutoVEM:
    AutoVEM
    suggested: None

    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

    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 10.1101/2020.12.24.424271 on bioRxiv