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Motivation: Bacteriophages (aka phages), which mainly infect bacteria, playkey roles in the biology of microbes. As the most abundant biological entitieson the planet, the number of discovered phages is only the tip of the iceberg.Recently, many new phages have been revealed using high throughput sequencing,particularly metagenomic sequencing. Compared to the fast accumulation ofphage-like sequences, there is a serious lag in taxonomic classification ofphages. High diversity, abundance, and limited known phages pose greatchallenges for taxonomic analysis. In particular, alignment-based tools havedifficulty in classifying fast accumulating contigs assembled from metagenomicdata. Results: In this work, we present a novel semi-supervised learning model,named PhaGCN, to conduct taxonomic classification for phage contigs. In thislearning model, we construct a knowledge graph by combining the DNA sequencefeatures learned by convolutional neural network (CNN) and protein sequencesimilarity gained from gene-sharing network. Then we apply graph convolutionalnetwork (GCN) to utilize both the labeled and unlabeled samples in training toenhance the learning ability. We tested PhaGCN on both simulated and realsequencing data. The results clearly show that our method competes favorablyagainst available phage classification tools.

Bacteriophage classification for assembled contigs using Graph Convolutional Network