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Biol."],"published-print":{"date-parts":[[2020,3]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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\u2010based and gene homology\u2010based methods are not efficient in identifying unknown viruses or short viral sequences from metagenomic data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Here we developed a reference\u2010free and alignment\u2010free machine learning method, DeepVirFinder, for identifying viral sequences in metagenomic data using deep learning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Trained based on sequences from viral RefSeq discovered before May 2015, and evaluated on those discovered after that date, DeepVirFinder outperformed the state\u2010of\u2010the\u2010art 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\u2010represented. 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s40484-019-0187-4","type":"journal-article","created":{"date-parts":[[2020,1,23]],"date-time":"2020-01-23T00:24:27Z","timestamp":1579739067000},"page":"64-77","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":680,"title":["Identifying viruses from metagenomic data using deep learning"],"prefix":"10.1002","volume":"8","author":[{"given":"Jie","family":"Ren","sequence":"first","affiliation":[{"name":"<!--1--> Quantitative and Computational Biology Program University of Southern California Los Angeles CA 90089 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Song","sequence":"additional","affiliation":[{"name":"<!--2--> School of Mathematics and Statistics Qingdao University Qingdao 266071 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Deng","sequence":"additional","affiliation":[{"name":"<!--1--> Quantitative and Computational Biology Program University of Southern California Los Angeles CA 90089 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nathan A.","family":"Ahlgren","sequence":"additional","affiliation":[{"name":"<!--3--> Department of Biology Clark University Worcester MA 01610 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jed A.","family":"Fuhrman","sequence":"additional","affiliation":[{"name":"<!--4--> Department of Biological Sciences University of Southern California Los Angeles CA 90089 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"<!--5--> Department of Computer Science University of California Irvine CA 92697 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohui","family":"Xie","sequence":"additional","affiliation":[{"name":"<!--5--> Department of Computer Science University of California Irvine CA 92697 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan","family":"Poplin","sequence":"additional","affiliation":[{"name":"<!--6--> Google Inc. 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Ahlgren, Jed A. Fuhrman, Yi Li, Xiaohui Xie, Ryan Poplin and Fengzhu Sun declare that they have no conflicts of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethics Guidelines"}},{"value":"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.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethics Guidelines"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}