DeepHost: phage host prediction with convolutional neural network

• Published in: Briefings in bioinformatics 2022-01, Vol.23 (1)
• Main Authors: Ruohan, Wang, Xianglilan, Zhang, Jianping, Wang, Shuai Cheng, L I
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Bacteriophages - genetics; Datasets; Genomes; High-Throughput Nucleotide Sequencing; Homology; Mutation; Neural networks; Neural Networks, Computer; Next-generation sequencing; Phages; Predictions; Taxonomy
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbab385

Abstract Next-generation sequencing expands the known phage genomes rapidly. Unlike culture-based methods, the hosts of phages discovered from next-generation sequencing data remain uncharacterized. The high diversity of the phage genomes makes the host assignment task challenging. To solve the issue, we proposed a phage host prediction tool—DeepHost. To encode the phage genomes into matrices, we design a genome encoding method that applied various spaced $k$-mer pairs to tolerate sequence variations, including insertion, deletions, and mutations. DeepHost applies a convolutional neural network to predict host taxonomies. DeepHost achieves the prediction accuracy of 96.05% at the genus level (72 taxonomies) and 90.78% at the species level (118 taxonomies), which outperforms the existing phage host prediction tools by 10.16–30.48% and achieves comparable results to BLAST. For the genomes without hits in BLAST, DeepHost obtains the accuracy of 38.00% at the genus level and 26.47% at the species level, making it suitable for genomes of less homologous sequences with the existing datasets. DeepHost is alignment-free, and it is faster than BLAST, especially for large datasets. DeepHost is available at https://github.com/deepomicslab/DeepHost.