Microbiome
Open source for microbiome bioinformatics
Contact: Leo Lahti
Key developers/contributors include Felix G.M. Ernst, Domenick C. Braccia, Ruizhu Huang, Tuomas Borman, Hector Corrada Bravo, Vasilis Dakos, Christopher Quince, Karoline Faust, Sudarshan Shetty, and others.
The full list of contributors varies over time, and is identified through the individual repositories. Check each individual package for acknowledgements, contact information and references. Don't hesitate to contact us for feedback, support, or feature requests.
MicrobiomeExperiment for R/Bioc
The MicrobiomeExperiment is an emerging ecosystem of R classes, methods, and educational material for microbiome research, formally introduced in European Bioconductor Meeting (EuroBioC2020). This work is now under active development as a joint work by multiple teams. New contributors and early adopters are very welcome. A greater project landing page will follow later :-)We are envisioning to rethink the microbiome research in R/Bioc. The new ecosystem contains the following key elements:
- Class (Bioc): MicrobiomeExperiment
- Methods (R packages): mia for analysis, miaViz for visualization, other tools are on the way
- Documentation: MiaBook manual
- Community: join us at MicrobiomeExperiment channel in Bioconductor Slack
Related resources
The Github site lists several tools for microbiome analysis that are on the way to being adapted to the new class structure, including:
- The microbiome R package extends the phyloseq data format and includes a comprehensive tutorial for large-scale microbiome analysis and visualization
- The early warnings toolkit to analyze shifts between alternative stable states in complex (eco)systems
- The seqtime for time series analysis
- The microbiomeutilities for microbiome data wrangling
- NMGS neutral models for microbial metagenomics
- RPA Robust Probabilistic Averaging
- CONCOCT Binning metagenomic contigs by coverage and composition