Classification of Neisseria meningitidis genomes with a bag-of-words approach and machine learning

• Published in: iScience 2024-03, Vol.27 (3), p.109257, Article 109257
• Main Authors: Podda, Marco, Bonechi, Simone, Palladino, Andrea, Scaramuzzino, Mattia, Brozzi, Alessandro, Roma, Guglielmo, Muzzi, Alessandro, Priami, Corrado, Sîrbu, Alina, Bodini, Margherita
• Format: Article
• Language: English
• Subjects: Classification of bioinformatical subject; Machine learning; Microbial genomics
• ISSN: 2589-0042; 2589-0042
• DOI: 10.1016/j.isci.2024.109257

Whole genome sequencing of bacteria is important to enable strain classification. Using entire genomes as an input to machine learning (ML) models would allow rapid classification of strains while using information from multiple genetic elements. We developed a “bag-of-words” approach to encode, using SentencePiece or k-mer tokenization, entire bacterial genomes and analyze these with ML. Initial model selection identified SentencePiece with 8,000 and 32,000 words as the best approach for genome tokenization. We then classified in Neisseria meningitidis genomes the capsule B group genotype with 99.6% accuracy and the multifactor invasive phenotype with 90.2% accuracy, in an independent test set. Subsequently, in silico knockouts of 2,808 genes confirmed that the ML model predictions aligned with our current understanding of the underlying biology. To our knowledge, this is the first ML method using entire bacterial genomes to classify strains and identify genes considered relevant by the classifier. [Display omitted] •We recoded bacterial genomes as words and classified them using machine learning•We tested the approach by predicting the type of bacterial capsule and invasiveness•Predictions were accurate: 99.6% for bacterial capsule type, 90.2% for invasiveness•This can improve tasks like monitoring antibiotic resistance and disease outbreaks Microbial genomics; Classification of bioinformatical subject; Machine learning