The microbiomeutilities is a supporting R package for the parent microbiome R/BioC package. This utility tool includes functions for formatting and visualization of phyloseq object. The package has a function
microbiome_pipeline, which generates an HTML report with infromation on preliminary QC, Alpha Diversity, Ordination and Composition analysis of OTU tables. The HTML report can be convenient for having prelimanry insights into the data.
Example output of the
The package provides access to a subset of studies included in the MicrobiomeHD database from Duvallet et al 2017: Meta-analysis of gut microbiome studies identifies disease-specific and shared responses. Nature communications. These datasets are converted to phyloseq objects and can be directly used in R environment.
Direction for this package
Depending on the real world usefulness, practicality and success, we plan to include complete or parts of this package in the Microbiome R package.
"Leo Lahti, Sudarshan Shetty et al. (Bioconductor, 2017). Tools for microbiome analysis in R.
The microbiome R package relies on the independently developed
phyloseq package and data structures for R-based microbiome analysis developed by Paul McMurdie and Susan Holmes.
ggplot2 H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2009.
Microbiome package website with step-wise tutorials:
More useful resources:
- Ben J. Callahan and Colleagues: Bioconductor Workflow for Microbiome Data Analysis: from raw reads to community analyses.
- Comeau AM and Colleagues: Microbiome Helper: a Custom and Streamlined Workflow for Microbiome Research
- MicrobiomeHD A standardized database of human gut microbiome studies in health and disease Case-Control
- Rhea A pipeline with modular R scripts
- Phyloseq Import, share, and analyze microbiome census data using R
About the Author
- Callahan, B. J., McMurdie, P. J. & Holmes, S. P. (2017). Exact sequence variants should replace operational taxonomic units in marker gene data analysis. bioRxiv, 113597.
- Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A. & Holmes, S. P. (2016). DADA2: high-resolution sample inference from Illumina amplicon data. Nature methods 13, 581-583.
- 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. (2010). QIIME allows analysis of high-throughput community sequencing data. Nature methods 7, 335-336.
- 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. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and environmental microbiology 75, 7537-7541.
Team, R. C. (2000). R language definition. Vienna, Austria: R foundation for statistical computing.
- Duvallet, Claire, et al. "Meta-analysis of gut microbiome studies identifies disease-specific and shared responses." Nature communications 8.1 (2017): 1784.
- Son, J. et al. Comparison of fecal microbiota in children with autism spectrum disorders and neurotypical siblings in the simons simplex collection. PLoS ONE 10, e0137725 (2015).
- Kang, D. W. et al. Reduced incidence of Prevotella and other fermenters in intestinal microflora of autistic children. PLoS ONE8, e68322 (2013).
- Schubert, A. M. et al. Microbiome data distinguish patients with clostridium difficile infection and non-c. difficile-associated diarrhea from healthy controls. mBio 5, e01021–14–e01021–14 (2014).
- Youngster, I. et al. Fecal microbiota transplant for relapsing clostridium difficile infection using a frozen inoculum from unrelated donors: a randomized, open-label, controlled pilot study. Clin. Infect. Dis. 58, 1515–1522 (2014).
- Baxter, N. T., Ruffin, M. T., Rogers, M. A. & Schloss, P. D. Microbiota-based model improves the sensitivity of fecal immunochemical test for detecting colonic lesions. Genome Med. 8, 37 (2016).
- Zackular, Joseph P., et al. "The gut microbiome modulates colon tumorigenesis." MBio 4.6 (2013): e00692-13.
- Zeller, G. et al. Potential of fecal microbiota for early-stage detection of colorectal cancer. Mol. Syst. Biol. 10, 766–766 (2014).
- Singh, P. et al. Intestinal microbial communities associated with acute enteric infections and disease recovery. Microbiome 3, 45 (2015).
- Noguera-Julian, M. et al. Gut microbiota linked to sexual preference and hiv infection. EBioMedicine 5, 135–146 (2016). Dinh, D. M. et al. Intestinal microbiota, microbial translocation, and systemic inflammation in chronic HIV infection. J. Infect. Dis. 211, 19–27 (2014).
- Lozupone, C. A. et al. Alterations in the gut microbiota associated with hiv-1 infection. Cell Host Microbe 14, 329–339 (2013).
- Gevers, D. et al. The treatment-naive microbiome in new-onset crohn’s disease. Cell Host Microbe 15, 382–392 (2014).
- Zhang, Z. et al Large-scale survey of gut microbiota associated with MHE via 16s rRNA-based pyrosequencing. Am. J. Gastroenterol. 108, 1601–1611 (2013).
- Wong, J. M. W., Souza, R. De, Kendall, C. W. C., Emam, A. & Jenkins, D. J. A. Colonic health: fermentation and short chain fatty acids. J. Clin. Gastroenterol. 40, 235–243 (2006).
- Ross, M. C. et al. 16s gut community of the cameron county hispanic cohort. Microbiome 3, 7 (2015).
- Zupancic, M. L. et al. Analysis of the gut microbiota in the old order Amish and its relation to the metabolic syndrome. PLoS ONE 7, e43052 (2012).
- Scher, J. U. et al. Expansion of intestinal prevotella copri correlates with enhanced susceptibility to arthritis. eLife 2, e01202 (2013).
- Alkanani, A. K. et al. Alterations in intestinal microbiota correlate with susceptibility to type 1 diabetes. Diabetes 64, 3510–3520 (2015).
- Scheperjans, F. et al Gut microbiota are related to parkinson’s disease and clinical phenotype. Mov. Disord. 30, 350–358 (2014).
The aim of this package is not to replace any of the other tools mentioned on this site. Instead this package is useful for a quick and (not so) dirty analysis of the OTU tables/biom files generated by tools such as QIIME (the newer QIIME2) (Caporaso, Kuczynski, Stombaugh et al., 2010), Mothur (Schloss, Westcott, Ryabin et al., 2009), DADA2 (Callahan, McMurdie, Rosen et al., 2016). Using the HTML report as a reference for more thorough analysis.