Supporting data for "DeePhage: distinguish virulent and temperate phage-derived sequences in metavirome data with a deep learning approach"

Prokaryotic viruses referred to as phages can be divided into virulent and temperate phages. Distinguishing virulent and temperate phage-derived sequences in metavirome data is important for their different roles in interactions with bacterial hosts and regulations of microbial communities. However, there is no experimental or computational approach to effectively classify their sequences in culture-independent metavirome. We present a new computational method DeePhage, which can directly and rapidly judge each read or contig as a virulent or temperate phage-derived fragment. Findings: DeePhage utilizes a one-hot encoding form to represent DNA sequences in detail. Sequence signatures are detected via a convolutional neural network to obtain valuable local features. The accuracy of DeePhage on five-fold cross validation reaches as high as 89%, nearly 10% and 30% higher than that of two similar tools, PhagePred and PHACTS. On real metavirome, DeePhage correctly predicts the highest proportion of contigs when using BLAST as annotation, without apparent preferences. Besides, DeePhage reduces running time than PhagePred and PHACTS by 245 and 810 times under the same computational configuration. By direct detection of the temperate viral fragments from metagenome and metavirome, we furthermore propose a new strategy to explore phage transformations in the microbial community. The ability to detect such transformations provides us a new insight into the potential treatment for human disease. Conclusions: DeePhage is a novel tool developed to rapidly and efficiently identify two kinds of phage fragments especially for metagenomics analysis. DeePhage is freely available via the Zhu Lab webpage or the GitHub repository.