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phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data
Open accessAbstract
BACKGROUND: the analysis of microbial communities through dna sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. RESULTS: Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. CONCLUSIONS: The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.
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Identification of Bacteria in Two Food Waste Black Soldier Fly Larvae Rearing Residues
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SARS-CoV-2 infection and viral load are associated with the upper respiratory tract microbiome
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Integrative Analysis of the Intestinal Metabolome of Childhood Asthma
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Mobile antimicrobial resistance genes in probiotics
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Respiratory microbiota: addressing clinical questions, informing clinical practice
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Selective targeting of skin pathobionts and inflammation with topically applied lactobacilli
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Retracted: Gut Microbiome Signatures of Risk and Prodromal Markers of Parkinson Disease
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Callaghan, B. L., Fields, A., Gee, D. G., Gabard-Durnam, L., Caldera, C., Humphreys, K. L., Goff, B., Flannery, J., Telzer, E. H., Shapiro, M., et al. (2020) · Development and Psychopathology · doi:10.1017/s0954579419000087 · Cited by 93
Oral and Gut Dysbiosis Leads to Functional Alterations in Parkinson’s Disease
Jo, S., Kang, W., Hwang, Y. S., Lee, S. H., Park, K. W., Kim, M. S., Lee, H., Yoon, H. J., Park, Y. K., Chalita, M., et al. (2022) · npj Parkinson s Disease · doi:10.1038/s41531-022-00351-6 · Cited by 85
A combined meta-barcoding and shotgun metagenomic analysis of spontaneous wine fermentation
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Microbial hitchhikers harbouring antimicrobial-resistance genes in the riverine plastisphere
Zadjelovic, V., Wright, R. J., Borsetto, C., Quartey, J., Cairns, T. N., Langille, M. G. I., Wellington, E. M. H., & Christie-Oleza, J. A. (2023) · Microbiome · doi:10.1186/s40168-023-01662-3 · Cited by 87
Nature-derived microbiota exposure as a novel immunomodulatory approach
Nurminen, N., Lin, J., Grönroos, M., Puhakka, R., Kramna, L., Vari, H. K., Viskari, H., Oikarinen, S., Roslund, M., Parajuli, A., et al. (2018) · Future Microbiology · doi:10.2217/fmb-2017-0286 · Cited by 80
Shaping of Intestinal Microbiota in Nlrp6- and Rag2-Deficient Mice Depends on Community Structure
Gálvez, E. J. C., Iljazovic, A., Gronow, A., Flavell, R., & Strowig, T. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.12.027 · Cited by 81
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Association of dietary patterns with the gut microbiota in older, community-dwelling men
Shikany, J. M., Demmer, R. T., Johnson, A. J., Fino, N. F., Meyer, K., Ensrud, K. E., Lane, N. E., Orwoll, E. S., Kado, D. M., Zmuda, J. M., et al. (2019) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqz174 · Cited by 83
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Geographic Differences in Gut Microbiota Composition Impact Susceptibility to Enteric Infection
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Profiling the salivary microbiome of the qatari population
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“4 Statistical analysis Alpha diversity was calculated using four different measures using the “phyloseq” package in R [19].”
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Gut microbiota signature in treatment-naïve attention-deficit/hyperactivity disorder
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Estrogen protection against EAE modulates the microbiota and mucosal-associated regulatory cells
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Diet may influence the oral microbiome composition in cats
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Organic nitrogen rearranges both structure and activity of the soil-borne microbial seedbank
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Cholestasis induced by bile duct ligation promotes changes in the intestinal microbiome in mice
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“2)) [41] and ComplexHeatmap [42], and custom-made scripts.”
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Fecal microbiota transplantation in clinical practice: Present controversies and future prospects
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Metadata
- Authors
- McMurdie, P. J., & Holmes, S.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-04-22
- Volume
- 8
- Issue
- 4
- Pages
- e61217
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2056279562
- Cited by
- 21,322
- References
- 11
- FWCI
- 58.63
- Type
- journal article
- Language
- en
- Open access
- gold
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.
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.