We develop software tools to help us analyze multi-omics data and make sense of the microbial world. All of our code is open source and available on Github. Click on the badges below to learn more about the software we develop.
Command-Line Tool for Processing 16S rRNA Gene Sequence Data
User-Friendly R Package for Supervised Machine Learning Pipelines
An interface to build machine learning models for classification
and regression problems.
mikropml implements the ML pipeline described
by Topçuoğlu et al. (2020) with reasonable
default options for data preprocessing, hyperparameter tuning,
cross-validation, testing, model evaluation, and interpretation steps.
See the website for more information,
documentation, and examples.
Schloss Lab Tools for Reproducible Microbiome Research 💩
A collection of useful functions and example code created and used by the Schloss Lab for reproducible microbiome research. Perform common tasks like read output files from mothur, tidy up your microbiome data, and format rmarkdown documents for publication. See the website for more information, documentation, and examples.