Using artificial intelligence to document the hidden RNA virosphere
Open accessPublished version of a preprint
Abstract
Current metagenomic tools can fail to identify highly divergent RNA viruses. We developed a deep learning algorithm, termed LucaProt, to discover highly divergent RNA-dependent RNA polymerase (RdRP) sequences in 10,487 metatranscriptomes generated from diverse global ecosystems. LucaProt integrates both sequence and predicted structural information, enabling the accurate detection of RdRP sequences. Using this approach, we identified 161,979 potential RNA virus species and 180 RNA virus supergroups, including many previously poorly studied groups, as well as RNA virus genomes of exceptional length (up to 47,250 nucleotides) and genomic complexity. A subset of these novel RNA viruses was confirmed by RT-PCR and RNA/DNA sequencing. Newly discovered RNA viruses were present in diverse environments, including air, hot springs, and hydrothermal vents, with virus diversity and abundance varying substantially among ecosystems. This study advances virus discovery, highlights the scale of the virosphere, and provides computational tools to better document the global RNA virome.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability
Katoh, K., & Standley, D. M. (2013) · Molecular Biology and Evolution · doi:10.1093/molbev/mst010 · Cited by 46,209 · see citing works
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Finding and evaluating community structure in networks
Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931 · see citing works
Evolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,523 · see citing works
Potter, S. C., Luciani, A., Eddy, S. R., Park, Y., Lopez, R., & Finn, R. D. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky448 · Cited by 2,607 · see citing works
Forecasting covid-19 new cases using deep learning methods
Xu, L., Magar, R., & Barati Farimani, A. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105342 · Cited by 105 · see citing works
Viroid-like colonists of human microbiomes
Zheludev, I. N., Edgar, R. C., Lopez-Galiano, M. J., de la Peña, M., Babaian, A., Bhatt, A. S., & Fire, A. Z. (2024) · Cell · doi:10.1016/j.cell.2024.09.033 · Cited by 73 · see citing works
Viroid-like colonists of human microbiomes
Zheludev, I. N., Edgar, R. C., Lopez-Galiano, M. J., de la Peña, M., Babaian, A., Bhatt, A. S., & Fire, A. Z. (2024) · doi:10.1101/2024.01.20.576352 · Cited by 73 · see citing works
Flagged first, then by citations
- Authors
- Hou, X., He, Y., Fang, P., Mei, S.-Q., Xu, Z., Wu, W.-C., Tian, J.-H., Zhang, S., Zeng, Z.-Y., Gou, Q.-Y., Xin, G.-Y., Le, S.-J., Xia, Y.-Y., Zhou, Y.-L., Hui, F.-M., Pan, Y.-F., Eden, J.-S., Yang, Z.-H., Han, C., Shu, Y.-L., Guo, D., Li, J., Holmes, E. C., Li, Z.-R., & Shi, M.
- Affiliations
- Sun Yat-sen University (CN)
- Alibaba Group (China) (CN)
- Wuhan Center for Disease Control and Prevention (CN)
- The University of Sydney (AU)
- Guangzhou University (CN)
- Guangzhou Laboratory (CN)
- City University of Hong Kong (HK)
- Corresponding author
- Holmes, Edward C.
- Li, Zhao-Rong
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2024-11
- Volume
- 187
- Issue
- 24
- Pages
- 6929-6942.e16
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W4403245162
- Cited by
- 162
- References
- 8
- Citations by year
- 2026: 55 · 2025: 79 · 2024: 27 · 2023: 1
- FWCI
- 44.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cell (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- CityU Scholars (submittedVersion, cc-by)
- Funders
- Guangdong Provincial Natural Science Foundation
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Environmental Science › Ecology › Bacteriophages and microbial interactions
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Endocrinology › Plant and Fungal Interactions Research
- Keywords
- RNA viruses, RNA-dependent RNA polymerase, deep learning, metatranscriptomics, virus discovery, RNA virome, viral diversity, hot springs, hydrothermal vents, airborne viruses
- MeSH terms
- Deep Learning, Virome, Algorithms, Artificial Intelligence, Phylogeny, RNA-Dependent RNA Polymerase, RNA Viruses, RNA, Viral, Genome, Viral, Hot Springs, Metagenomics, Hydrothermal Vents
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
