The classification of pathogens in emerging and re-emerging viruses represents major interests in taxonomic studies, functional genomics, host-pathogen interplay, prevention, and disease treatments. It consists of assigning a given sequence to its related group of known sequences sharing similar characteristics and traits. The challenges to such classification could be associated with several virus properties including recombination, mutation rate, multiplicity of motifs, and diversity. In domains such as pathogen monitoring and surveillance, it is important to detect and quantify known and novel taxa without exploiting the full and accurate alignments or virus family profiles. In this study, we propose an alignment-free method, CASTOR-KRFE, to detect discriminating subsequences within known pathogen sequences to classify accurately unknown pathogen sequences. This method includes three major steps: (1) vectorization of known viral genomic sequences based on k-mers to constitute the potential features, (2) efficient way of pattern extraction and evaluation maximizing classification performance, and (3) prediction of the minimal set of features fitting a given criterion (threshold of performance metric and maximum number of features). We assessed this method through a jackknife data partitioning on a dozen of various virus data sets, covering the seven major virus groups and including influenza virus, Ebola virus, human immunodeficiency virus 1, hepatitis C virus, hepatitis B virus, and human papillomavirus. CASTOR-KRFE provides a weighted average F-measure >0.96 over a wide range of viruses. Our method also shows better performance on complex virus data sets than multiple subsequences extractor for classification (MISSEL), a subsequence extraction method, and the Discriminative mode of MEME patterns extraction tool.

No abstract

Machine 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.

No abstract