  Motivation: Bacteriophages (aka phages), which mainly infect bacteria, play
key roles in the biology of microbes. As the most abundant biological entities
on 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 of
phage-like sequences, there is a serious lag in taxonomic classification of
phages. High diversity, abundance, and limited known phages pose great
challenges for taxonomic analysis. In particular, alignment-based tools have
difficulty in classifying fast accumulating contigs assembled from metagenomic
data. Results: In this work, we present a novel semi-supervised learning model,
named PhaGCN, to conduct taxonomic classification for phage contigs. In this
learning model, we construct a knowledge graph by combining the DNA sequence
features learned by convolutional neural network (CNN) and protein sequence
similarity gained from gene-sharing network. Then we apply graph convolutional
network (GCN) to utilize both the labeled and unlabeled samples in training to
enhance the learning ability. We tested PhaGCN on both simulated and real
sequencing data. The results clearly show that our method competes favorably
against available phage classification tools.
