Compass
This dataset collects processed 16S / shotgun-derived microbiome tables formatted for machine learning. Each config is one benchmark task; column sets differ between configs, but within a config the train, validation, and test splits use the same schema.
Dataset summary
See our preprint for details
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| Config | Task theme | Typical use | Num examples |
|---|---|---|---|
mgnify-biomes |
MGnify sample → biome hierarchy | Biome prediction from genus-level abundances and study metadata | 41399 |
roswall |
Infant gut (PRJEB38986) | Timepoint and delivery mode prediction from gut profiles | 2031 |
handuo |
Species-in-context (drug exposure) | Classification with drug / ATC context and abundances | 3960 |
mastrorilli |
Drug degradation | Regression: degradation rate from community composition | 15419 |
Each row includes a Split field (train, validation, or test) consistent with the Hugging Face split you load.
Usage
This repository is gated. To use it you'll need to:
- Request access — click the "Request access" button at the top of this repo's page on Hugging Face. Requests are auto-approved.
- Authenticate — log in to Hugging Face from your environment so the download tooling can use your token:
huggingface-cli login
Or set the token directly:
export HF_TOKEN=hf_xxxxxxxxxxxxxxxxxxxx
You can create a token at https://huggingface.co/settings/tokens.
Once both steps are done, you can load the model/dataset normally:
from datasets import load_dataset
REPO_ID = "outpost-bio/Compass" # adjust if your namespace/name differs
# One benchmark (DatasetDict: train, validation, test)
ds = load_dataset(REPO_ID, "mgnify-biomes")
train = ds["train"]
val = ds["validation"]
test = ds["test"]
# Optional: default config (library-defined ordering)
ds_default = load_dataset(REPO_ID)
Column reference
Taxa: Taxa and Relative Abundances are parallel lists containing taxon identifiers and relative abundances
mgnify-biomes
- Context / labels:
Run Accession,Data Type,Sequencing Method,Pipeline Version,Study Accession,Biome 1…Biome 5(biome lineage). - Features:
Taxa,Relative Abundances - Split:
Split
roswall
- Sample / environment:
Sample ID(run accession),Timepoint,Delivery Mode - Features:
Taxa,Relative Abundances - Split:
Split
handuo
- Metadata:
SIC Name,Control,ATC Class - Features:
Taxa,Relative Abundances,Sample ID - Split:
Split
mastrorilli
- Targets / metadata:
Degradation Rate,Drug,Sample ID - Features:
Taxa,Relative Abundances - Split:
Split - Splits follow sample boundaries (no overlap of
Sample IDacross splits).
License
apache-2.0
References to public data used
Lorna Richardson, Ben Allen, Germana Baldi, Martin Beracochea, Maxwell L Bileschi, Tony Burdett, Josephine Burgin, Juan Caballero- Pérez, Guy Cochrane, Lucy J Colwell, Tom Curtis, Alejandra Escobar- Zepeda, Tatiana A Gurbich, Varsha Kale, Anton Korobeynikov, Shriya Raj, Alexander B Rogers, Ekaterina Sakharova, Santiago Sanchez, Dar- ren J Wilkinson, and Robert D Finn. Mgnify: the microbiome sequence data analysis resource in 2023. Nucleic Acids Research, 51(D1):D753– D759, 12 2022. ISSN 0305-1048. doi: 10.1093/nar/gkac1080. URL https://doi.org/10.1093/nar/gkac1080
Handuo Shi, Daniel P Newton, Taylor H Nguyen, Sylvie Estrela, Juan Sanchez, Michael Tu, Po-Yi Ho, Qinglin Zeng, Brian DeFelice, Justin Sonnenburg, and Kerwyn Casey Huang. Nutrient competition predicts gut microbiome restructuring under drug perturbations. August 2024.
Eleonora Mastrorilli, Pamela Herd, Federico E. Rey, Andrew L. Good- man, and Michael Zimmermann. Linking interpersonal differences in gut microbiota composition and drug biotransformation activity. bioRxiv, 2026. doi: 10.64898/2026.01.21.700809. URL https://www.biorxiv. org/content/early/2026/01/21/2026.01.21.700809.
Josefine Roswall, Lisa M Olsson, Petia Kovatcheva-Datchary, Staffan Nilsson, Valentina Tremaroli, Marie-Christine Simon, Pia Kiilerich, Rozita Akrami, Manuela Krämer, Mathias Uhlén, Anders Gummesson, Karsten Kristiansen, Jovanna Dahlgren, and Fredrik Bäckhed. Develop- mental trajectory of the healthy human gut microbiota during the first 5 years of life. Cell Host Microbe, 29(5):765–776.e3, May 2021.
Citation
Learning the Language of the Microbiome with Transformers
Neythen J Treloar, Saif Ur-Rehman, Jenny Yang
bioRxiv 2026.05.02.722381; doi: https://doi.org/10.64898/2026.05.02.722381
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