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Filtering by: Keyword Low biomass Keyword Quantitative metagenomics Subject Machine learning

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An improved workflow for accurate and robust healthcare environmental surveillance using metagenomics

Description:

Cite this article: Shen, J., McFarland, A.G., Blaustein, R.A. et al. An improved workflow for accurate and robust healthcare environmental surveillance using metagenomics. Microbiome 10, 206 (2022). https://doi.org/10.1186/s40168-022-01412-x and Background: Effective surveillance of microbial communities in the healthcare environment is increasingly important in infection prevention. Metagenomics-based techniques are promising due to their untargeted nature but are currently challenged by several limitations: (1) they are not powerful enough to extract valid signals out of the background noise for low-biomass samples,...

Keyword:

Quantification, Quantitative metagenomics, Low biomass, Viability, Machine learning, Environmental surveillance, Sequencing depth prediction, and Infection prevention

Subject:

Metagenomics, Quantitative research, Nosocomial infections--Prevention, Machine learning, and Environmental monitoring

Creator:

K. Allison Perry-Dow, Jiaxian Shen, Vincent B. Young, Ryan A. Blaustein, Mary K. Hayden, Alexander G. McFarland, Anahid A. Moghadam, Laura J. Rose, and Erica M. Hartmann

Contributor:

Centers for Disease Control and Prevention (funding agency)

Owner:

Jiaxian SHEN

Publisher:

Microbiome

Language:

English

Date Uploaded:

12/19/2022

Date Modified:

12/19/2022

Date Created:

2022-12-02

Resource Type:

Research Paper

Alternate Identifier:

DOI 10.1186/s40168-022-01412-x, PMCID PMC9716758, and PMID 36457108

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