  The recent development of metagenomic sequencing makes it possible to
sequence microbial genomes including viruses in an environmental sample.
Identifying viral sequences from metagenomic data is critical for downstream
virus analyses. The existing reference-based and gene homology-based methods
are not efficient in identifying unknown viruses or short viral sequences. Here
we have developed a reference-free and alignment-free machine learning method,
DeepVirFinder, for predicting viral sequences in metagenomic data using deep
learning techniques. DeepVirFinder was trained based on a large number of viral
sequences discovered before May 2015. Evaluated on the sequences after that
date, DeepVirFinder outperformed the state-of-the-art method VirFinder at all
contig lengths. Enlarging the training data by adding millions of purified
viral sequences from environmental metavirome samples significantly improves
the accuracy for predicting under-represented viruses. Applying DeepVirFinder
to real human gut metagenomic samples from patients with colorectal carcinoma
(CRC) identified 51,138 viral sequences belonging to 175 bins. Ten bins were
associated with the cancer status, indicating their potential use for
non-invasive diagnosis of CRC. In summary, DeepVirFinder greatly improved the
precision and recall rates of viral identification, and it will significantly
accelerate the discovery rate of viruses.
