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 algorithm for accelerated exploring any-length genomic fragments in sequences using Hamming distance between the binary-expressed characters of an RNA and query patterns. The found repetitive genomic sub-sequences of different lengths were placed on one plot as genomic trajectories (walks) to increase the effectiveness of geometrical multi-scale genomic studies. Primary attention was paid to the building and analysis of the atg -triplet walks composing the schemes or skeletons of the viral RNAs. The 1-D distributions of these codon-starting atg -triplets were built with the single-symbol walks for full-scale analyses. The visual examination was followed by calculating statistical parameters of genomic sequences, including the estimation of geometry deviation and fractal properties of inter- atg distances. This approach was applied to the SARS CoV-2, MERS CoV, Dengue and Ebola viruses, whose complete genomic sequences are taken from GenBank and GISAID databases. The relative stability of these distributions 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 of atg -distributions. The results of this work can found in classification of the virus families and in the study of their mutation.

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  1. Version published to 10.1101/2021.06.17.448868v3 on bioRxiv
  2. Version published to 10.37394/23201.2022.21.35
  3. Version published to 10.1101/2021.06.17.448868v2 on bioRxiv
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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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  5. Version published to 10.1101/2021.06.17.448868v1 on bioRxiv