Time Series Analysis of SARS-CoV-2 Genomes and Correlations among Highly Prevalent Mutations

  1. Neha Periwal
  2. Shravan B. Rathod
  3. Sankritya Sarma
  4. Gundeep S. Johar
  5. Avantika Jain
  6. Ravi P. Barnwal
  7. Kinsukh R. Srivastava
  8. Baljeet Kaur
  9. Pooja Arora
  10. Vikas Sood

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Abstract

We performed a meta-analysis on SARS-CoV-2 genomes categorized by collection month and identified several significant mutations. Pearson correlation analysis of these significant mutations identified 16 comutations having absolute correlation coefficients of >0.4 and a frequency of >30% in the genomes used in this study.

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  1. Version published to 10.1128/spectrum.01219-22
  2. ScreenIT

    SciScore for 10.1101/2022.04.05.487114: (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
    All the SARS-CoV-2 genomic sequences were collected in a month-wise manner (based on the sample collection month) from the Virus Pathogen Resource (ViPR) database [18].
    ViPR
    suggested: (vipR, RRID:SCR_010685)
    We created an empty matrix with 30000 columns and 55759 rows using the NumPy module of Python.
    NumPy
    suggested: (NumPy, RRID:SCR_008633)
    To make the visualization more effective, we represent the dendrogram with a heatmap using the pdist and squareform method of scipy library.
    scipy
    suggested: (SciPy, RRID:SCR_008058)
    All these analyses were performed in python.
    python
    suggested: (IPython, RRID:SCR_001658)
    Functional impacts of mutations: To investigate the effect of mutation on protein function, we used the widely popular PredictSNP web server [22] available at https://loschmidt.chemi.muni.cz/predictsnp/.
    PredictSNP
    suggested: (PredictSNP, RRID:SCR_006327)
    This web tool is composed of six different predictors, PhD-SNP, MAPP, SNAP, PolyPhen-1,
    SNAP
    suggested: (SNAP, RRID:SCR_007936)
    SIFT and PolyPhen-2 to predict whether mutation is deleterious or neutral.
    SIFT
    suggested: (SIFT, RRID:SCR_012813)
    PhD-SNP, MAPP, SNAP, PolyPhen-1,
    PhD-SNP
    suggested: (PhD-SNP, RRID:SCR_010782)
    , SIFT and PolyPhen-2 apply support vector machine, physicochemical characteristics and protein sequence alignment score, neural network approach, expert set of empirical rules, protein sequence alignment score and naïve Bayes respectively [22].
    PolyPhen-2
    suggested: None
    In addition to its own ΔΔG prediction, DynaMut also predicts ΔΔG using NMA based ENCoM (Elastic network contact model) [24] and, other structure-based predictors like mCSM [25], SDM [26] and DUET [27].
    mCSM
    suggested: (mCSM, RRID:SCR_010776)
    To calculate the nLMI of WT and MT proteins, we employed Python-based correlationplus 0.2.1 tool [33].
    Python-based
    suggested: None

    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.

    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 10.1101/2022.04.05.487114 on bioRxiv