Supporting data for "DeePVP: Identification and classification of phage virion proteins using deep learning"

Many biological properties of phages are determined by phage virion proteins (PVPs), and the poor annotation of PVPs is a bottleneck for many areas of viral research, such as viral phylogenetic analysis, viral host identification and antibacterial drug design. Because of the high diversity of PVP sequences, the PVP annotation of a phage genome remains a particularly challenging bioinformatic task.<br>Based on deep learning, we developed DeePVP. The main module of DeePVP aims to discriminate PVPs from non-PVPs within a phage genome, while the extended module of DeePVP can further classify predicted PVPs into the ten major classes of PVPs. Compared with the present state-of-the-art tools, the main module of DeePVP performs better, with a 9.05% higher <i>F1-score</i> in the PVP identification task. Moreover, the overall <i>accuracy</i> of the extended module of DeePVP in the PVP classification task is approximately 3.72% higher than that of PhANNs. Two application cases show that the predictions of DeePVP are more reliable and can better reveal the compact PVP-enriched region than the current state-of-the-art tools. Particularly, in the <i>Escherichia</i> phage phiEC1 genome, a novel PVP-enriched region that is conserved in many other <i>Escherichia</i> phage genomes was identified, indicating that DeePVP will be a useful tool for the analysis of phage genomic structures. <br>DeePVP outperforms state-of-the-art tools. The program is optimized in both a virtual machine with GUI and a docker so that the tool can be easily run by non-computer professionals.