The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
BugBase predicts organism-level microbiome phenotypes
Open accessAbstract
Shotgun metagenomics and marker gene amplicon sequencing can be used to directly measure or predict the functional repertoire of the microbiota en masse , but current methods do not readily estimate the functional capability of individual microorganisms. Here we present BugBase, an algorithm that predicts organism-level coverage of functional pathways as well as biologically interpretable phenotypes such as oxygen tolerance, Gram staining and pathogenic potential, within complex microbiomes using either whole-genome shotgun or marker gene sequencing data. We find BugBase’s organism-level pathway coverage predictions to be statistically higher powered than current ‘bag-of-genes’ approaches for discerning functional changes in both host-associated and environmental microbiomes.
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QIIME allows analysis of high-throughput community sequencing data
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Self-citationGreengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB
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Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences
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Self-citationHuman gut microbiome viewed across age and geography
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Self-citationRichness of human gut microbiome correlates with metabolic markers
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Regulation of prefrontal cortex myelination by the microbiota
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Cited by
The impact of skin care products on skin chemistry and microbiome dynamics
Bouslimani, A., da Silva, R., Kosciolek, T., Janssen, S., Callewaert, C., Amir, A., Dorrestein, K., Melnik, A. V., Zaramela, L. S., Kim, J.-N., et al. (2019) · BMC Biology · doi:10.1186/s12915-019-0660-6 · Cited by 234
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Metadata
- Authors
- Ward, T., Larson, J., Meulemans, J., Hillmann, B., Lynch, J., Sidiropoulos, D., Spear, J. R., Caporaso, G., Blekhman, R., Knight, R., Fink, R., & Knights, D.
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2017-05-02
- OpenAlex
- W2611146598
- Cited by
- 479
- References
- 12
- Type
- posted content
- Language
- en
- Open access
- green
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- Ward, T., Larson, J., Meulemans, J., Hillmann, B., Lynch, J., Sidiropoulos, D., Spear, J. R., Caporaso, G., Blekhman, R., Knight, R., Fink, R., & Knights, D. (2017). BugBase predicts organism-level microbiome phenotypes. bioRxiv (Cold Spring Harbor Laboratory). https://doi.org/10.1101/133462
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