ProkBERT PhaStyle: Accurate Phage Lifestyle Prediction with Pretrained Genomic Language Models
BackgroundPhage lifestyle prediction, i.e. classifying phage sequences as virulent or temperate, is crucial in biomedical and ecological applications. Phage sequences from metagenome or metavirome assemblies are often fragmented, and the diversity of environmental phages is not well known. Current computational approaches often rely on database comparisons and machine learning algorithms that require significant effort and expertise to update. We propose using genomic language models for phage lifestyle classification, allowing efficient direct analysis from nucleotide sequences without the need for sophisticated preprocessing pipelines or manually curated databases. MethodsWe trained three genomic language models (DNABERT-2, Nucleotide Transformer, and ProkBERT) on datasets of short, fragmented sequences. These models were then compared with dedicated phage lifestyle prediction methods (PhaTYP, DeePhage, BACPHLIP) in terms of accuracy, prediction speed, and generalization capability. ResultsProkBERT PhaStyle consistently outperforms existing models in various scenarios. It generalizes well for out-of-sample data, accurately classifies phages from extreme environments, and also demonstrates high inference speed. Despite having up to 20 times fewer parameters, it proved to be better performing than much larger genomic language models. ConclusionsGenomic language models offer a simple and computationally efficient alternative for solving complex classification tasks, such as phage lifestyle prediction. ProkBERT PhaStyles simplicity, speed, and performance suggest its utility in various ecological and clinical applications.