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

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.
Competing Interests: M.P. and S.B. disclose that their postdoctoral grant at the University of Pisa, Italy, is funded by GSK. Outside of the submitted work, S.B. also discloses having received grants from the University of Siena, Italy and the University of Tuscia, Viterbo, Italy; payment or honoraria for lectures, presentations, speakers’ bureaus, manuscript writing or educational events from the University of Siena, Italy, University of Bari, Italy, and the Italica Academy srl. A.P., A.B., G.R., A.M., and M.B. are employed by GSK. M.S. was an intern at GSK during the time of the study. G.R. holds shares in GSK and in Novartis AG. A.M. holds shares in GSK. C.P. and A.S. disclose that GSK commissioned this research. The authors declare no other financial and non-financial relationships and activities and no other conflicts of interest.
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