Bioconductor

Community-driven, global open-source project
Started in 2001 from genomics
High impact across bioinformatics

Community is the key

Training programs & workshops
Community support
Conferences

Software

~2,300 R packages
Review, testing, documentation

Microbiome Analysis (mia)

Microbiome data science ecosystem
“Downstream”, statistical analysis
mia package top 8.3% Bioconductor downloads

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Related ecosystems

Over the years, several approaches to data organization have been developed. One of the first widely adopted data containers in microbiome research was phyloseq (McMurdie and Holmes 2013), designed for 16S amplicon sequencing data. However, it has limitations, particularly in linking multiple experiments and ensuring interoperability between tools in Bioconductor.

SummarizedExperiment (SE) (Huber et al. 2015), on the other hand, was introduced as a general-purpose container for biological data and has become the most widely used format within the Bioconductor ecosystem. It has since been extended to more specialized formats, including SingleCellExperiment (Amezquita et al. 2020) for single-cell data and TreeSummarizedExperiment (TreeSE) (Huang et al. 2021), which is tailored for microbiome datasets.

Benefits

Tightly linked with broader Bioconductor ecosystem
Improved scalability
Improved multi-table integration

Community-driven ecosystem of tools

mia (Data analysis)
miaViz (Visualization)
miaSim (Simulation)
miaTime (Time series analysis)
miaDash (Graphical user interface)
iSEETree (Interactive visualization)
Expanded by independent developers

mia logo. MGnifyR logo. HoloFoodR logo. iSEE logo. MAE logo. SE logo. SCE logo. scater logo. benchdamic logo. netcomi logo. radEmu logo. DESeq2 logo. Biobakery logo. anansi logo.

Orchestrating Microbiome Analysis with Bioconductor

Resources and tutorials for microbiome analysis
Community-built best practices
Open to contributions!

Go to the Orchestrating Microbiome Analysis (OMA) online book

microbiome.github.io/OMA

Data containers

Data containers

References

Amezquita, Robert A, Aaron T L Lun, Etienne Becht, Vince J Carey, Lindsay N Carpp, Ludwig Geistlinger, Federico Marini, et al. 2020. “Orchestrating Single-Cell Analysis with Bioconductor.” Nature Methods 17 (2): 137–45. https://doi.org/10.1038/s41592-019-0654-x.

Huang, Ruizhu, Charlotte Soneson, Felix G. M. Ernst, et al. 2021. “TreeSummarizedExperiment: A S4 Class for Data with Hierarchical Structure.” F1000Research 9: 1246. https://doi.org/10.12688/f1000research.26669.2.

Huber, W., V. J. Carey, R. Gentleman, S. Anders, M. Carlson, B. S. Carvalho, H. C. Bravo, et al. 2015. “Orchestrating High-Throughput Genomic Analysis with Bioconductor.” Nature Methods 12 (2): 115–21. http://www.nature.com/nmeth/journal/v12/n2/full/nmeth.3252.html.

McMurdie, PJ, and S Holmes. 2013. “Phyloseq: An r Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data.” PLoS ONE 8: e61217. https://doi.org/10.1371/journal.pone.0061217.