Tracking mutational semantics of SARS-CoV-2 genomes

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.

Multimodal Biomedical Data Fusion Using Sparse Canonical Correlation Analysis and Cooperative Learning: A Cohort Study on COVID-19

“…Using NLP techniques, particularly the Word2Vec model for viral embedding, has been recognized in prior research. But while earlier studies were often unimodal and focused on tasks like viral classification or evolution tracking, our method differs by integrating this with other data modalities, offering a more comprehensive view [48][49][50][51] . The merit of this method is evident in its ability to encapsulate the cumulative effects of multiple viral mutations and their relationships, a task that single-modal approaches might find challenging.…”

VariantHunter: a method and tool for fast detection of emerging SARS-CoV-2 variants

“…The 4-week period has been chosen as the most appropriate trade-off that captures the weekly periodicity of data collections/submissions and allows extracting amounts of sequences that grant sufficient statistical significance for the trend measurements. One-month periods are typically employed in works that aim to track variants and provide early warning for them ( 14 , 17 , 18 ). The complete set of amino acid changes associated with genome sequences \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$M^{\prime}= \{ m|\exists s \in S^{\prime}\wedge m \in s.M\} $\end{document} is then derived and—for every mutation—levels of prevalence in the first, second, third and fourth week are computed \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\left( {{p_1},{p_2},{p_3},{p_4} \in \left[ {0,100} \right]} \right)$\end{document} .…”