Tracking mutational semantics of SARS-CoV-2 genomes

  1. Rohan Singh
  2. Sunil Nagpal
  3. Nishal K. Pinna
  4. Sharmila S. Mande

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Abstract

Natural language processing (NLP) algorithms process linguistic data in order to discover the associated word semantics and develop models that can describe or even predict the latent meanings of the data. The applications of NLP become multi-fold while dealing with dynamic or temporally evolving datasets (e.g., historical literature). Biological datasets of genome-sequences are interesting since they are sequential as well as dynamic. Here we describe how SARS-CoV-2 genomes and mutations thereof can be processed using fundamental algorithms in NLP to reveal the characteristics and evolution of the virus. We demonstrate applicability of NLP in not only probing the temporal mutational signatures through dynamic topic modelling, but also in tracing the mutation-associations through tracing of semantic drift in genomic mutation records. Our approach also yields promising results in unfolding the mutational relevance to patient health status, thereby identifying putative signatures linked to known/highly speculated mutations of concern.

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  1. Version published to 10.1038/s41598-022-20000-5
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    SciScore for 10.1101/2021.12.21.21268187: (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
    A python wrapper for Dynamic Topic Models (DTM) present in gensim26 python package (version 3.8) was used on top of compiled binaries of DTM from the original paper6 to execute DTM on our corpus.
    python
    suggested: (IPython, RRID:SCR_001658)

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    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2021.12.21.21268187v1 on medRxiv