The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Identifying viruses from metagenomic data using deep learning
Open accessAbstract
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.
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Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Fast and sensitive protein alignment using DIAMOND
Buchfink, B., Xie, C., & Huson, D. H. (2015) · Nature Methods · doi:10.1038/nmeth.3176 · Cited by 14,201
The Pfam protein families database in 2019
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., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky995 · Cited by 4,602
Kraken: ultrafast metagenomic sequence classification using exact alignments
Wood, D. E., & Salzberg, S. L. (2014) · Genome Biology · doi:10.1186/gb-2014-15-3-r46 · Cited by 4,521
PHASTER: a better, faster version of the PHAST phage search tool
Arndt, D., Grant, J. R., Marcu, A., Sajed, T., Pon, A., Liang, Y., & Wishart, D. S. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw387 · Cited by 3,557
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
Alipanahi, B., Delong, A., Weirauch, M. T., & Frey, B. J. (2015) · Nature Biotechnology · doi:10.1038/nbt.3300 · Cited by 2,873
Predicting effects of noncoding variants with deep learning–based sequence model
Zhou, J., & Troyanskaya, O. G. (2015) · Nature Methods · doi:10.1038/nmeth.3547 · Cited by 2,338
MetaPhlAn2 for enhanced metagenomic taxonomic profiling
Truong, D. T., Franzosa, E. A., Tickle, T. L., Scholz, M., Weingart, G., Pasolli, E., Tett, A., Huttenhower, C., & Segata, N. (2015) · Nature Methods · doi:10.1038/nmeth.3589 · Cited by 2,185
A universal SNP and small-indel variant caller using deep neural networks
Poplin, R., Chang, P.-C., Alexander, D., Schwartz, S., Colthurst, T., Ku, A., Newburger, D., Dijamco, J., Nguyen, N., Afshar, P. T., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4235 · Cited by 1,840 · cited 1×
“…of non-coding variants [24,25], predicting chromatin accessibility [26], calling genetic variants from millions of short reads in NGS samples [27], predicting methylation quantitative trait loci [28], identifying evolutionary conserved sequences [29], and identifying enhancers and promoters…”
BackgroundSelf-citationGut microbiome development along the colorectal adenoma-carcinoma sequence
Feng, Q., Liang, S., Jia, H., Stadlmayr, A., Tang, L., Lan, Z., Zhang, D., Xia, H., Xu, X., Jie, Z., et al. (2015) · Nature Communications · doi:10.1038/ncomms7528 · Cited by 1,506
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Profiling the human intestinal environment under physiological conditions
Shalon, D., Culver, R. N., Grembi, J. A., Folz, J., Treit, P. V., Shi, H., Rosenberger, F. A., Dethlefsen, L., Meng, X., Yaffe, E., et al. (2023) · Nature · doi:10.1038/s41586-023-05989-7 · Cited by 484
Large language models improve annotation of prokaryotic viral proteins
Flamholz, Z. N., Biller, S. J., & Kelly, L. (2024) · Nature Microbiology · doi:10.1038/s41564-023-01584-8 · Cited by 92
Peng, Y., Lu, Z., Pan, D., Shi, L.-D., Zhao, Z., Liu, Q., Zhang, C., Jia, K., Li, J., Hubert, C. R. J., et al. (2023) · The ISME Journal · doi:10.1038/s41396-023-01491-0 · Cited by 48
Du, S., Tong, X., Lai, A. C. K., Chan, C. K., Mason, C. E., & Lee, P. K. H. (2023) · Nature Communications · doi:10.1038/s41467-023-38400-0 · Cited by 44
ProkBERT family: genomic language models for microbiome applications
Ligeti, B., Szepesi-Nagy, I., Bodnár, B., Ligeti-Nagy, N., & Juhász, J. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1331233 · Cited by 30
“We assessed our classification accuracy against INHERIT, VirSorter2, and DeepVirFinder ( Ren et al., 2020).”
MethodInfluential
Metadata
- Authors
- Ren, J., Song, K., Deng, C., Ahlgren, N. A., Fuhrman, J. A., Li, Y., Xie, X., Poplin, R., & Sun, F.
- Venue
- Quantitative Biology
- Publisher
- Wiley
- Published
- 2020-03
- Volume
- 8
- Issue
- 1
- Pages
- 64-77
- ISSN
- 2095-4689 (ISSN-L)
- 2095-4697
- OpenAlex
- W3003110834
- Cited by
- 659
- References
- 55
- FWCI
- 36.31
- Type
- journal article
- Language
- en
- Open access
- diamond
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