The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Rhea: a transparent and modular R pipeline for microbial profiling based on 16S rRNA gene amplicons
Open accessAbstract
The importance of 16S rRNA gene amplicon profiles for understanding the influence of microbes in a variety of environments coupled with the steep reduction in sequencing costs led to a surge of microbial sequencing projects. The expanding crowd of scientists and clinicians wanting to make use of sequencing datasets can choose among a range of multipurpose software platforms, the use of which can be intimidating for non-expert users. Among available pipeline options for high-throughput 16S rRNA gene analysis, the R programming language and software environment for statistical computing stands out for its power and increased flexibility, and the possibility to adhere to most recent best practices and to adjust to individual project needs. Here we present the Rhea pipeline, a set of R scripts that encode a series of well-documented choices for the downstream analysis of Operational Taxonomic Units (OTUs) tables, including normalization steps, alpha - and beta -diversity analysis, taxonomic composition, statistical comparisons, and calculation of correlations. Rhea is primarily a straightforward starting point for beginners, but can also be a framework for advanced users who can modify and expand the tool. As the community standards evolve, Rhea will adapt to always represent the current state-of-the-art in microbial profiles analysis in the clear and comprehensive way allowed by the R language. Rhea scripts and documentation are freely available at https://lagkouvardos.github.io/Rhea .
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R: A Language and Environment for Statistical Computing
R Core Team (2025) · doi:10.32614/r.manuals · Cited by 352,181
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 48,949
QIIME allows analysis of high-throughput community sequencing data
Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon, J. I., et al. (2010) · Nature Methods · doi:10.1038/nmeth.f.303 · Cited by 34,887
phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data
McMurdie, P. J., & Holmes, S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061217 · Cited by 21,322
Search and clustering orders of magnitude faster than BLAST
Edgar, R. C. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq461 · Cited by 20,826
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., Lesniewski, R. A., Oakley, B. B., Parks, D. H., Robinson, C. J., et al. (2009) · Applied and Environmental Microbiology · doi:10.1128/aem.01541-09 · Cited by 19,549
UPARSE: highly accurate OTU sequences from microbial amplicon reads
Edgar, R. C. (2013) · Nature Methods · doi:10.1038/nmeth.2604 · Cited by 16,707
SIMPSON, E. H. (1949) · Nature · doi:10.1038/163688a0 · Cited by 14,164
A new method for non-parametric multivariate analysis of variance
Anderson, M. J. (2001) · Austral Ecology · doi:10.1111/j.1442-9993.2001.01070.pp.x · Cited by 13,828
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Just, S., Mondot, S., Ecker, J., Wegner, K., Rath, E., Gau, L., Streidl, T., Hery-Arnaud, G., Schmidt, S., Lesker, T. R., et al. (2018) · Microbiome · doi:10.1186/s40168-018-0510-8 · Cited by 269
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Krenkel, O., Hundertmark, J., Abdallah, A. T., Kohlhepp, M., Puengel, T., Roth, T., Branco, D. P. P., Mossanen, J. C., Luedde, T., Trautwein, C., et al. (2020) · Gut · doi:10.1136/gutjnl-2019-318382 · Cited by 220
Metadata
- Authors
- Lagkouvardos, I., Fischer, S., Kumar, N., & Clavel, T.
- Venue
- PeerJ
- Publisher
- PeerJ
- Published
- 2017-01-11
- Volume
- 5
- Pages
- e2836
- ISSN
- 2167-8359 (ISSN-L)
- OpenAlex
- W2578250538
- Cited by
- 429
- References
- 44
- FWCI
- 10.68
- Type
- journal article
- Language
- en
- Open access
- gold
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