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Identifying viruses from metagenomic data using deep learning

Quantitative Biology 2020年第1期8卷 64-77页

作者： Jie Ren Kai Song Chao Deng Nathan AAhlgren jed afuhrman Yi Li Xiaohui Xie Ryan Poplin Fengzhu Sun Quantitative and Computational Biology Program University of Southern CaliforniaLos AngelesCA 90089USA School of Mathematics and Statistics Qingdao UniversityQingdao 266071China Department of Biology Clark UniversityWorcesterMA 01610USA Department of Biological Sciences University of Southern CaliforniaLos AngelesCA 90089USA Department of Computer Science University of CaliforniaIrvineCA 92697USA Google Inc. Mountain ViewCA 94043USA

Background:The recent development of metagenomic sequencing makes it possible to massively sequence microbial genomes including viral genomes without the need for laboratory *** reference-based and gene homology-based methods are not efficient in identifying unknown viruses or short viral sequences from metagenomic ***:Here we developed a reference-free and alignment-free machine learning method,DeepVirFinder,for identifying viral sequences in metagenomic data using deep ***'.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 *** the training data with additional millions of purified viral sequences from metavirome samples further improved the accuracy for identifying virus groups that are *** 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 ***: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.

关键词： metagenome deep learning virus identification machine learning

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