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

References

[1] Norman, J. M., Handley, S. A., Baldridge, M. T., Droit, L., Liu, C. Y., Keller, B. C., Kambal, A., Monaco, C. L., Zhao, G., Fleshner, P., (2015) Disease-specific alterations in the enteric virome in inflammatory bowel disease. Cell, 160, 447–460

[2] Reyes, A., Blanton, L. V., Cao, S., Zhao, G., Manary, M., Trehan, I., Smith, M. I., Wang, D., Virgin, H. W., Rohwer, F., (2015) Gut DNA viromes of Malawian twins discordant for severe acute malnutrition. Proc. Natl. Acad. Sci. USA, 112, 11941–11946

[3] Ma, Y., You, X., Mai, G., Tokuyasu, T. and Liu, C. (2018) A human gut phage catalog correlates the gut phageome with type 2 diabetes. Microbiome, 6, 24

[4] Roux, S., Enault, F., Hurwitz, B. L. and Sullivan, M. B. (2015) VirSorter: mining viral signal from microbial genomic data. PeerJ, 3, e985

[5] Ren, J., Ahlgren, N. A., Lu, Y. Y., Fuhrman, J. A. and Sun, F. (2017) VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data. Microbiome, 5, 69

[6] Amgarten, D., Braga, L. P. P., da Silva, A. M. and Setubal, J. C. (2018) Marvel, a tool for prediction of bacteriophage sequences in metagenomic bins. Front. Genet., 9, 304

[7] Roux, S., Faubladier, M., Mahul, A., Paulhe, N., Bernard, A., Debroas, D. and Enault, F. (2011) Metavir: a web server dedicated to virome analysis. Bioinformatics, 27, 3074–3075

[8] Rampelli, S., Soverini, M., Turroni, S., Quercia, S., Biagi, E., Brigidi, P. and Candela, M. (2016) ViromeScan: a new tool for metagenomic viral community profiling. BMC Genomics, 17, 165

[9] Wommack, K. E., Bhavsar, J., Polson, S. W., Chen, J., Dumas, M., Srinivasiah, S., Furman, M., Jamindar, S. and Nasko, D. J. (2012) VIROME: a standard operating procedure for analysis of viral metagenome sequences. Stand. Genomic Sci., 6, 427–439

[10] Wood, D. E. and Salzberg, S. L. (2014) Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol., 15, R46

[11] Kim, D., Song, L., Breitwieser, F. P. and Salzberg, S. L. (2016) Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res., 26, 1721–1729

[12] Truong, D. T., Franzosa, E. A., Tickle, T. L., Scholz, M., Weingart, G., Pasolli, E., Tett, A., Huttenhower, C. and Segata, N. (2015) MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods, 12, 902–903

[13] Buchfink, B., Xie, C. and Huson, D. H. (2015) Fast and sensitive protein alignment using DIAMOND. Nat. Methods, 12, 59–60

[14] Fouts, D. E. (2006) Phage_Finder: automated identification and classification of prophage regions in complete bacterial genome sequences. Nucleic Acids Res., 34, 5839–5851

[15] Lima-Mendez, G., Van Helden, J., Toussaint, A. and Leplae, R. (2008) Prophinder: a computational tool for prophage prediction in prokaryotic genomes. Bioinformatics, 24, 863–865

[16] Akhter, S., Aziz, R. K. and Edwards, R. A. (2012) PhiSpy: a novel algorithm for finding prophages in bacterial genomes that combines similarity- and composition-based strategies. Nucleic Acids Res., 40, e126

[17] Arndt, D., Grant, J. R., Marcu, A., Sajed, T., Pon, A., Liang, Y. and Wishart, D. S. (2016) PHASTER: a better, faster version of the PHAST phage search tool. Nucleic Acids Res., 44, W16–W 21

[18] Roux, S., Hallam, S. J., Woyke, T. and Sullivan, M. B. (2015) Viral dark matter and virus-host interactions resolved from publicly available microbial genomes. eLife, 4, e08490

[19] Paez-Espino, D., Pavlopoulos, G. A., Ivanova, N. N. and Kyrpides, N. C. (2017) Nontargeted virus sequence discovery pipeline and virus clustering for metagenomic data. Nat. Protoc., 12, 1673–1682

[20] Alipanahi, B., Delong, A., Weirauch, M. T. and Frey, B. J. (2015) Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol., 33, 831–838

[21] Zeng, H., Edwards, M. D., Liu, G. and Gifford, D. K. (2016) Convolutional neural network architectures for predicting DNA-protein binding. Bioinformatics, 32, i121–i127

[22] Quang, D. and Xie, X. (2019) Factornet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data. Methods,166, 40–47

[23] Wang, M., Tai, C., E, W. and Wei, L. (2018) DeFine: deep convolutional neural networks accurately quantify intensities of transcription factor-DNA binding and facilitate evaluation of functional non-coding variants. Nucleic Acids Res., 46, e69

[24] Zhou, J. and Troyanskaya, O. G. (2015) Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods, 12, 931–934

[25] Quang, D. and Xie, X. (2016) DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Res., 44, e107

[26] Kelley, D. R., Snoek, J. and Rinn, J. L. (2016) Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res., 26, 990–999

[27] Poplin, R., Chang, P.-C., Alexander, D., Schwartz, S., Colthurst, T., Ku, A., Newburger, D., Dijamco, J., Nguyen, N., Afshar, P. T., (2018) A universal SNP and small-indel variant caller using deep neural networks. Nat. Biotechnol., 36, 983–987

[28] Zeng, H. and Gifford, D. K. (2017) Predicting the impact of non-coding variants on DNA methylation. Nucleic Acids Res., 45, e99

[29] Li, Y., Quang, D. and Xie, X. (2017) Understanding sequence conservation with deep learning. In: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp. 400–406. ACM

[30] Li, Y., Shi, W. and Wasserman, W. W. (2018) Genome-wide prediction of cis-regulatory regions using supervised deep learning methods. BMC Bioinformatics.  19,  202

[31] Singh, S., Yang, Y., Poczos, B. and Ma, J. (2019) Predicting enhancer-promoter interaction from genomic sequence with deep neural networks. Quant. Biol.   7,  122–137

[32] Yue, T. and Wang, H. (2018) Deep learning for genomics: A concise overview. arXiv:1802.00810

[33] Lauring, A. S., Frydman, J. and Andino, R. (2013) The role of mutational robustness in RNA virus evolution. Nat. Rev. Microbiol., 11, 327–336

[34] Glenn, T. C. (2011) Field guide to next-generation DNA sequencers. Mol. Ecol. Resour., 11, 759–769

[35] World Health Organization. (2014) World Cancer Report 2014. Stewart, B., Wild, C. P., eds., IAIC

[36] Hawk, E.T. and Levin, B. (2016) Colorectal cancer prevention. J. Clinic. Oncolo. 23, 378–391

[37] Feng, Q., Liang, S., Jia, H., Stadlmayr, A., Tang, L., Lan, Z., Zhang, D., Xia, H., Xu, X., Jie, Z., (2015) Gut microbiome development along the colorectal adenoma-carcinoma sequence. Nat. Commun., 6, 6528

[38] Vogtmann, E., Hua, X., Zeller, G., Sunagawa, S., Voigt, A. Y., Hercog, R., Goedert, J. J., Shi, J., Bork, P. and Sinha, R. (2016) Colorectal cancer and the human gut microbiome: reproducibility with whole-genome shotgun sequencing. PLoS One, 11, e0155362

[39] Nakatsu, G., Li, X., Zhou, H., Sheng, J., Wong, S. H., Wu, W. K. K., Ng, S. C., Tsoi, H., Dong, Y., Zhang, N., (2015) Gut mucosal microbiome across stages of colorectal carcinogenesis. Nat. Commun., 6, 8727

[40] Zeller, G., Tap, J., Voigt, A. Y., Sunagawa, S., Kultima, J. R., Costea, P. I., Amiot, A., Böhm, J., Brunetti, F., Habermann, N., (2014) Potential of fecal microbiota for early-stage detection of colorectal cancer. Mol. Syst. Biol., 10, 766

[41] Lu, Y. Y., Chen, T., Fuhrman, J. A. and Sun, F. (2017) COCACOLA: binning metagenomic contigs using sequence COmposition, read CoverAge, CO-alignment and paired-end read LinkAge. Bioinformatics, 33, 791–798

[42] Dutilh, B. E., Cassman, N., McNair, K., Sanchez, S. E., Silva, G. G., Boling, L., Barr, J. J., Speth, D. R., Seguritan, V., Aziz, R. K., (2014) A highly abundant bacteriophage discovered in the unknown sequences of human faecal metagenomes. Nat. Commun., 5, 4498

[43] El-Gebali, S., Mistry, J., Bateman, A., Eddy, S. R., Luciani, A., Potter, S. C., Qureshi, M., Richardson, L. J., Salazar, G. A., Smart, A., (2018) The pfam protein families database in 2019. Nucleic Acids Res. D427–D432

[44] Zheng, T., Li, J., Ni, Y., Kang, K., Misiakou, M.-A., Imamovic, L., Chow, B. K. C., Rode, A. A., Bytzer, P., Sommer, M., (2019) Mining, analyzing, and integrating viral signals from metagenomic data. Microbiome, 7, 42

[45] Edwards, R. A., McNair, K., Faust, K., Raes, J. and Dutilh, B. E. (2016) Computational approaches to predict bacteriophage-host relationships. FEMS Microbiol. Rev., 40, 258–272

[46] Ahlgren, N. A., Ren, J., Lu, Y. Y., Fuhrman, J. A. and Sun, F. (2017) Alignment-free d2* oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences. Nucleic Acids Res., 45, 39–53

[47] Gouy, M. and Gautier, C. (1982) Codon usage in bacteria: correlation with gene expressivity. Nucleic Acids Res., 10, 7055–7074

[48] Sharp, P. M., Rogers, M. S. and McConnell, D. J. (1985) Selection pressures on codon usage in the complete genome of bacteriophage T7. J. Mol. Evol., 21, 150–160

[49] Pride, D. T., Wassenaar, T. M., Ghose, C. and Blaser, M. J. (2006) Evidence of host-virus co-evolution in tetranucleotide usage patterns of bacteriophages and eukaryotic viruses. BMC Genomics, 7, 8

[50] Carbone, A. (2008) Codon bias is a major factor explaining phage evolution in translationally biased hosts. J. Mol. Evol., 66, 210–223

[51] Ponsero, A. J. and Hurwitz, B. L. (2019) The promises and pitfalls of machine learning for detecting viruses in aquatic metagenomes. Front. Microbiol., 10, 806

[52] Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J. and Man’e, D. (2016) Concrete problems in AI safety. arXiv:1606.06565

[53] Hendrycks, D. and Gimpel, K. A (2017) A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: Proceedings of  International Conference on Learning Representations 2017. Toulon

[54] Lakshminarayanan, B., Pritzel, A. and Blundell, C. (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. In: Proceedings of Advances in Neural Information Processing Systems, pp. 6402–6413

[55] Liang, S., Li, Y. and Srikant, R. (2017) Enhancing the reliability of out-of-distribution image detection in neural networks. arXiv:1706.02690

[56] Hendrycks, D., Mazeika, M. and Dietterich, T. G. (2018) Deep anomaly detection with outlier exposure. arXiv:1812.04606

[57] Shafaei, A., Schmidt, M. and Little, J. J. (2018) Does your model know the digit 6 is not a cat? a less biased evaluation of outlier detectors. arXiv:1809.04729

[58] Ren, J., Liu, P. J., Fertig, E., Snoek, J., Poplin, R., DePristo, M. A., Dillon, J. V. and Lakshminarayanan, B. (2019) Likelihood ratios for out-of-distribution detection. arXiv:1906.02845

[59] Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J. V., Lakshminarayanan, B. and Snoek, J. (2019) Can you trust your model’s uncertainty? Evaluating predictive uncertainty under dataset shift. arXiv:1906.02530

[60] Nalisnick, E., Matsukawa, A., Teh, Y. W. and Lakshminarayanan, B. (2019) Detecting out-of-distribution inputs to deep generative models using a test for typicality. arXiv:1906.02994

[61] Kingma, D. P. and Ba, J. (2015) Adam: A method for stochastic optimization. In: Proceedings of the 3rd International Conference for Learning Representations. San Diego

[62] Minot, S., Sinha, R., Chen, J., Li, H., Keilbaugh, S. A., Wu, G. D., Lewis, J. D. and Bushman, F. D. (2011) The human gut virome: inter-individual variation and dynamic response to diet. Genome Res., 21, 1616–1625

[63] Roux, S., Brum, J. R., Dutilh, B. E., Sunagawa, S., Duhaime, M. B., Loy, A., Poulos, B. T., Solonenko, N., Lara, E., Poulain, J., (2016) Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature, 537, 689–693

[64] Fang, Z., Tan, J., Wu, S., Li, M., Xu, C., Xie, Z. and Zhu, H. (2019) PPR-Meta: a tool for identifying phages and plasmids from metagenomic fragments using deep learning. Gigascience, 8, giz066

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