ADToolbox
From raw amplicon reads to a calibrated anaerobic digestion model.
ADToolbox is developed in the Chan Lab at Colorado State University. It connects metagenomics evidence, curated reaction and feed databases, and dynamic ADM1 / e-ADM simulations into one reproducible Python and command-line workflow.
Anaerobic digestion models such as ADM1 lump the microbial community into a handful of guilds whose initial biomass you are expected to guess. ADToolbox replaces that guess with measurement: it takes 16S amplicon data, maps it through GTDB and a curated enzyme-to-reaction database, and produces the microbial COD allocation the model needs.
📖 Read the documentation
Install
pip install adtoolbox # base install, Python 3.11+
pip install "adtoolbox[optimize]" # + parameter-tuning backends
pip install "adtoolbox[dashboard]" # + interactive Dash/Escher visualization
Or use the container, which bundles fastp, VSEARCH, MMseqs2, and the SRA Toolkit:
docker run --rm parsaghadermazi/adtoolbox:latest adtoolbox --help
See the installation guide for source installs, extras, external tools, and HPC notes.
Quick start
# 1. Download the reference databases
adtoolbox database download-all-databases --output-dir ./database
# 2. Turn amplicon samples into microbial COD allocations
adtoolbox metagenomics process \
--input ./samples.tsv --input-type sra --assay amplicon \
--output-dir ./process --sra-dir ./sra \
--database-dir ./database --execute
# Use the direct functional route for shotgun reads
adtoolbox metagenomics process \
--input ./shotgun_samples.tsv --input-type reads --assay shotgun \
--output-dir ./process --database-dir ./database --execute
# 3. Simulate
adtoolbox adm e-adm --models-json reference_data/models.json --report csv
The full walkthrough is in the Quickstart.
Documentation map
| Page | Contents |
|---|---|
| Quickstart | Install, databases, first simulation, first pipeline run. |
| Metagenomics Pipeline | Sample tables, execution profiles, Slurm, output schemas. |
| ADM Models | Input contracts, stoichiometry, rate laws, inhibition terms. |
| Parameter Tuning | Fitting kinetic parameters to experimental data. |
| CLI | Every command and option. |
| Python API | Generated reference for all modules. |
Try it without installing
The example notebooks run on Binder. Note that Escher map visualization is not available there.
Building the docs locally
pip install -r docs/requirements.txt
mkdocs serve
Contact
Developed in the Chan Lab at Colorado State University.
| Parsa Ghadermazi | parsa.ghadermazi@colostate.edu |
| Ethan Rimelman | rimelman@colostate.edu |
| Siu Hung Joshua Chan (PI) | joshua.chan@colostate.edu |
1 maintainer
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