Orchestrating Microbiome Analysis with Bioconductor

Bioconductor Africa Seminar Series, May 2026

Wednesday, May 6, 2026

About me

Tuomas Borman

Doctoral Researcher at the University of Turku, Finland (Turku Data Science Group)
Developing microbiome data science R/Bioconductor packages

European Bioconductor Conference 2026 in Turku!

eurobioc2026.bioconductor.org

Topics

Week 1. Ecosystems
Week 2. End-to-end workflow
Week 3. Statistical analysis

Microbiome data science ecosystems

Bioconductor

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

Collaborative development

Software & data repository
Tutorials & documentation
Community support & events

Open (microbiome) data science ecosystems

Bioconductor software

~2,300 R packages
Review, testing, documentation

Bioconductor 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.

Orchestrating Microbiome Analysis, OMA

Microbiome data science ecosystem
“Downstream”, statistical analysis
Microbiome Analysis (mia): > 15,000 yearly downloads from distinct IPs (top 8.05% in Bioconductor)

Bioconductor sticker

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

bioconductor.org/books/release/OMA/

Take home message

Community is the key

Data containers

The core of software
Structured, standardized way to manage complex data
Enables modular, efficient workflows

Optimal container for microbiome data?

Multiple assays: seamless interlinking

Optimal container for microbiome data?

Multiple assays: seamless interlinking
Hierarchical data: supporting samples & features

Optimal container for microbiome data?

Multiple assays: seamless interlinking
Hierarchical data: supporting samples & features
Side information: extended capabilities & data types

Optimal container for microbiome data?

Multiple assays: seamless interlinking
Hierarchical data: supporting samples & features
Side information: extended capabilities & data types
Optimized: for speed & memory

Optimal container for microbiome data?

Multiple assays: seamless interlinking
Hierarchical data: supporting samples & features
Side information: extended capabilities & data types
Optimized: for speed & memory
Integrated: with other applications & frameworks

TreeSummarizedExperiment

(Huang et al. 2021)

SummarizedExperiment

(Huber et al. 2015)

Take home messages

Community is the key
(Tree)SummarizedExperiment is a structured way to handle complex (biological) data

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.