Identifying viruses from metagenomic data using deep learning

TLDR

Metagenomic sequencing now enables large‑scale sequencing of microbial genomes, including viruses, without culture, yet existing reference‑based and homology‑based methods struggle to detect unknown or short viral sequences. The study develops a reference‑free, alignment‑free machine learning method, DeepVirFinder, to identify viral sequences in metagenomic data. DeepVirFinder employs deep learning to analyze sequences without relying on reference genomes or alignment. DeepVirFinder achieved higher AUROC than VirFinder across all contig lengths (0.93–0.98), improved accuracy with expanded training data, and identified over 51,000 viral sequences in colorectal carcinoma patient gut samples, with ten viral bins linked to cancer status, demonstrating its utility for viral discovery in metagenomics.

Abstract

Background The recent development of metagenomic sequencing makes it possible to massively sequence microbial genomes including viral genomes without the need for laboratory culture. Existing reference‐based and gene homology‐based methods are not efficient in identifying unknown viruses or short viral sequences from metagenomic data. Methods Here we developed a reference‐free and alignment‐free machine learning method, DeepVirFinder, for identifying viral sequences in metagenomic data using deep learning. Results Trained based on sequences from viral RefSeq discovered before May 2015, and evaluated on those discovered after that date, DeepVirFinder outperformed the state‐of‐the‐art method VirFinder at all contig lengths, achieving AUROC 0.93, 0.95, 0.97, and 0.98 for 300, 500, 1000, and 3000 bp sequences respectively. Enlarging the training data with additional millions of purified viral sequences from metavirome samples further improved the accuracy for identifying virus groups that are under‐represented. Applying DeepVirFinder to real human gut metagenomic samples, we identified 51,138 viral sequences belonging to 175 bins in patients with colorectal carcinoma (CRC). Ten bins were found associated with the cancer status, suggesting viruses may play important roles in CRC. Conclusions Powered by deep learning and high throughput sequencing metagenomic data, DeepVirFinder significantly improved the accuracy of viral identification and will assist the study of viruses in the era of metagenomics.