  Motivation: Bacteriophages are viruses infecting bacteria. Being key players
in microbial communities, they can regulate the composition/function of
microbiome by infecting their bacterial hosts and mediating gene transfer.
Recently, metagenomic sequencing, which can sequence all genetic materials from
various microbiome, has become a popular means for new phage discovery.
However, accurate and comprehensive detection of phages from the metagenomic
data remains difficult. High diversity/abundance, and limited reference genomes
pose major challenges for recruiting phage fragments from metagenomic data.
Existing alignment-based or learning-based models have either low recall or
precision on metagenomic data. Results: In this work, we adopt the
state-of-the-art language model, Transformer, to conduct contextual embedding
for phage contigs. By constructing a protein-cluster vocabulary, we can feed
both the protein composition and the proteins' positions from each contig into
the Transformer. The Transformer can learn the protein organization and
associations using the self-attention mechanism and predicts the label for test
contigs. We rigorously tested our developed tool named PhaMer on multiple
datasets with increasing difficulty, including quality RefSeq genomes, short
contigs, simulated metagenomic data, mock metagenomic data, and the public
IMG/VR dataset. All the experimental results show that PhaMer outperforms the
state-of-the-art tools. In the real metagenomic data experiment, PhaMer
improves the F1-score of phage detection by 27\%.
