Viruses of the phylum
Our knowledge of viral sequence space has exploded with advancing sequencing technologies and large-scale sampling and analytical efforts. Though archaea are important and abundant prokaryotes in many systems, our knowledge of archaeal viruses outside of extreme environments is limited. This largely stems from the lack of a robust, high-throughput, and systematic way to distinguish between bacterial and archaeal viruses in datasets of curated viruses. Here we upgrade our prior text-based tool (MArVD) via training and testing a random forest machine learning algorithm against a newly curated dataset of archaeal viruses. After optimization, MArVD2 presented a significant improvement over its predecessor in terms of scalability, usability, and flexibility, and will allow user-defined custom training datasets as archaeal virus discovery progresses. Benchmarking showed that a model trained with viral sequences from the hypersaline, marine, and hot spring environments correctly classified 85% of the archaeal viruses with a false detection rate below 2% using a random forest prediction threshold of 80% in a separate benchmarking dataset from the same habitats.
more » « less- Award ID(s):
- 2149505
- NSF-PAR ID:
- 10446500
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- ISME Communications
- Volume:
- 3
- Issue:
- 1
- ISSN:
- 2730-6151
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Background With the advent of metagenomics, the importance of microorganisms and how their interactions are relevant to ecosystem resilience, sustainability, and human health has become evident. Cataloging and preserving biodiversity is paramount not only for the Earth’s natural systems but also for discovering solutions to challenges that we face as a growing civilization. Metagenomics pertains to the in silico study of all microorganisms within an ecological community in situ
, however, many software suites recover only prokaryotes and have limited to no support for viruses and eukaryotes.Results In this study, we introduce the
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VEBA software suite allows for the in silico recovery of microorganisms from all domains of life by integrating cutting edge algorithms in novel ways.VEBA fully integrates both end-to-end and task-specific metagenomic analysis in a modular architecture that minimizes dependencies and maximizes productivity. The contributions ofVEBA to the metagenomics community includes seamless end-to-end metagenomics analysis but also provides users with the flexibility to perform specific analytical tasks.VEBA allows for the automation of several metagenomics steps and shows that new information can be recovered from existing datasets. -
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https://github.com/dyxstat/ViralCC .
