DeePhafier: a phage lifestyle classifier using a multilayer self-attention neural network combining protein information

• Published in: Briefings in bioinformatics Vol. 25; no. 5
• Main Authors: Miao, Yan, Sun, Zhenyuan, Lin, Chen, Gu, Haoran, Ma, Chenjing, Liang, Yingjian, Wang, Guohua
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 25.07.2024; Oxford Publishing Limited (England)
• Subjects: Algorithms; Bacteriophages - genetics; Classification; Computational Biology - methods; Microbiomes; Multilayers; Neural networks; Neural Networks, Computer; Phages; Problem Solving Protocol; Protein folding; Proteins; Viral Proteins - genetics; Viral Proteins - metabolism; self-attention network; phage lifestyle classification; metagenome; PSSM matrix
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbae377

Abstract Bacteriophages are the viruses that infect bacterial cells. They are the most diverse biological entities on earth and play important roles in microbiome. According to the phage lifestyle, phages can be divided into the virulent phages and the temperate phages. Classifying virulent and temperate phages is crucial for further understanding of the phage–host interactions. Although there are several methods designed for phage lifestyle classification, they merely either consider sequence features or gene features, leading to low accuracy. A new computational method, DeePhafier, is proposed to improve classification performance on phage lifestyle. Built by several multilayer self-attention neural networks, a global self-attention neural network, and being combined by protein features of the Position Specific Scoring Matrix matrix, DeePhafier improves the classification accuracy and outperforms two benchmark methods. The accuracy of DeePhafier on five-fold cross-validation is as high as 87.54% for sequences with length >2000bp.