Machine learning-based approach KEVOLVE efficiently identifies SARS-CoV-2 variant-specific genomic signatures

Abstract

AbstractMachine learning has proven to be a powerful tool for the identification of distinctive genomic signatures among viral sequences. Such signatures are motifs present in the viral genome that differentiate species or variants. In the context of SARS-CoV-2, the identification of such signatures can contribute to taxonomic and phylogenetic studies, help in recognizing and defining distinct emerging variants, and focus the characterization of functional properties of polymorphic gene products. Here, we study KEVOLVE, an approach based on a genetic algorithm with a machine learning kernel, to identify several genomic signatures based on minimal sets of k-mers. In a comparative study, in which we analyzed large SARS-CoV-2 genome dataset, KEVOLVE performed better in identifying variant-discriminative signatures than several gold-standard reference statistical tools. Subsequently, these signatures were characterized to highlight potential biological functions. The majority were associated with known mutations among the different variants, with respect to functional and pathological impact based on available literature. Notably, we found show evidence of new motifs, specifically in the Omicron variant, some of which include silent mutations, indicating potentially novel, variant-specific virulence determinants. The source code of the method and additional resources are available at: https://github.com/bioinfoUQAM/KEVOLVE.Author summaryAdvances in cloning and sequencing technologies have yielded a vast repository of viral genomic sequence data. To analyze this complex and massive data, Machine learning, which refers to the development and application of computer algorithms that improve with experience, has proven to be efficient. Although many methods have been developed to classify viruses into different characteristic groups, it is often difficult to explain the predictions of these methods. To overcome this, we are working in our laboratory on the design of machine learning based methods for discriminative signatures identification within viral genomic sequences. These signatures which are a specific motifs to groups of viruses known to be pervasive in their genome, are used to 1) build accurate and explainable prediction tools for pathogens and 2) highlight mutations potentially associated with functional changes. In this paper we present the potential of our latest approach KEVOLVE. We first compare it to three discriminating motif identification tools with data sets covering several SARS-CoV-2 variant genomes. We then focus on the identified motifs by KEVOLVE to analyze the mutations associated with the different variants and the potential changes in biological functions that they may involve.