Identifying viruses from metagenomic data using deep learning

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.

Graphical abstract

Keywords

metagenome / deep learning / virus identification / machine learning

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SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at https://doi.org/10.1007/s40484-019-0187-4.

ACKNOWLEDGEMENTS

The research was supported by the U.S. National Institutes of Health R01GM120624, National Science Foundation DMS-1518001, National Natural Science Foundation of China (11701546), and the Simons Collaboration on Computational Biogeochemical Modeling of Marine Ecosystems (CBIOMES; grant ID 549943). We thank Drs. Michael S. Waterman, Gesine Reinert, Ying Wang, Rui Jiang, Yang Lu, Lizzie Dorfman, Mr. Weili Wang, and Mr. Luigi Manna for helpful discussions and suggestions. We thank USC Center for High Performance Computing (HPC) for helping us use their cluster computers.

COMPLIANCE WITH ETHICS GUIDELINES

The authors Jie Ren, Kai Song, Chao Deng, Nathan A. Ahlgren, Jed A. Fuhrman, Yi Li, Xiaohui Xie, Ryan Poplin and Fengzhu Sun declare that they have no conflicts of interest.

All procedures performed in studies were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

NOTE TO RELATED WORK OF FANG et al. [⁶⁴]

A preliminary version of this manuscript was put in arXiv (arxiv.org/abs/1806.07810) on June 20, 2018. During the process of the submission to regular journals, Fang et al. [⁶⁴] used deep learning to classify metagenomic fragments to chromosomal, viral and plasmid sequences. Similar prediction accuracy for viruses using nucleotide base encoding as presented in this paper was obtained. The two studies should be considered independent.

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