DeepPL: A deep-learning-based tool for the prediction of bacteriophage lifecycle

Bacteriophages (phages) are viruses that infect bacteria and can be classified into two different lifecycles. Virulent phages (or lytic phages) have a lytic cycle that can lyse the bacteria host after their infection. Temperate phages (or lysogenic phages) can integrate their phage genomes into bacterial chromosomes and replicate with bacterial hosts via the lysogenic cycle. Identifying phage lifecycles is a crucial step in developing suitable applications for phages. Compared to the complicated traditional biological experiments, several tools have been designed for predicting phage lifecycle using different algorithms, such as random forest (RF), linear support-vector classifier (SVC), and convolutional neural network (CNN). In this study, we developed a natural language processing (NLP)-based tool-DeepPL-for predicting phage lifecycles via nucleotide sequences. The test results showed that our DeepPL had an accuracy of 94.65% with a sensitivity of 92.24% and a specificity of 95.91%. Moreover, DeepPL had 100% accuracy in lifecycle prediction on the phages we isolated and biologically verified previously in the lab. Additionally, a mock phage community metagenomic dataset was used to test the potential usage of DeepPL in viral metagenomic research. DeepPL displayed a 100% accuracy for individual phage complete genomes and high accuracies ranging from 71.14% to 100% on phage contigs produced by various next-generation sequencing technologies. Overall, our study indicates that DeepPL has a reliable performance on phage lifecycle prediction using the most fundamental nucleotide sequences and can be applied to future phage and metagenomic research. Bacteriophages are viruses that infect bacteria and play a critical role in the microbial community within different environments via phage-bacterial evolutionary interactions. The classification of phage lifecycles is of great importance in deploying the potential applications of phages and better understanding complex microbial interactions. However, the traditional biological methods for phage lifecycle identification are complicated and time-consuming. In this study, we proposed a deep learning-based tool-DeepPL-for predicting phage lifecycles using the phage nucleotide genome. Compared with other bioinformatic tools, DeepPL was developed from the pre-trained transformers model-DNABERT-designed for fundamental nucleotide language combined with representative phage complete genomes. Our in-house biological results were further used to verify the output of DeepPL. Overall, DeepPL performs with high precision for phage lifecycle prediction and could contribute to the genomic data-driven direction of phage research and applications.