Visual and Quantitative Analyses of Virus Genomic Sequences using a Metric-based Algorithm

  1. Alexandra Belinsky
  2. Guennadi A. Kouzaev

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Abstract

This work aims to study the virus RNAs using a novel accelerated algorithm to explore any-length repetitive genomic fragments in sequences using Hamming distance between the binary-expressed characters of an RNA and a query pattern. Primary attention is paid to the building and analyzing 1-D distributions (walks) of atg-patterns - codon-starting triplets in genomes. These triplets compose a distributed set called a word scheme of RNA. A complete genome map is built by plotting the mentioned atg-walks, trajectories of separate (a-, c-, g-, and t-symbols) nucleotides, and the lines designating the genomic words. The said map can be additionally equipped by gene’s designations making this tool pertinent for multi-scale genomic analyses. The visual examination of atg-walks is followed by calculating statistical parameters of genomic sequences, including estimating walk- geometry deviation of RNAs and fractal properties of word-length distributions. This approach is applied to the SARS CoV-2, MERS CoV, Dengue, and Ebola viruses, whose complete genomic sequences are taken from GenBank and GISAID. The relative stability of these walks for SARS CoV-2 and MERS CoV viruses was found, unlike the Dengue and Ebola distributions that showed an increased deviation of their geometrical and fractal characteristics. The developed approach can be useful in further studying mutations of viruses and building their phylogenic trees.

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  1. Version published to 10.37394/23201.2022.21.35
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    SciScore for 10.1101/2021.06.17.448868: (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

    No key resources detected.

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

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

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  3. Version published to 10.1101/2021.06.17.448868 on bioRxiv