https://github.com/monarch-initiative/phenopacket-store
Collections of GA4GH phenopackets that represent individuals with Mendelian diseases.
https://github.com/monarch-initiative/phenopacket-store
ga4gh hpo monarchinitiative phenopackets
Last synced: 7 days ago
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Collections of GA4GH phenopackets that represent individuals with Mendelian diseases.
- Host: GitHub
- URL: https://github.com/monarch-initiative/phenopacket-store
- Owner: monarch-initiative
- License: bsd-3-clause
- Created: 2022-12-19T13:30:51.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2025-10-03T06:31:20.000Z (8 days ago)
- Last Synced: 2025-10-03T08:34:19.517Z (8 days ago)
- Topics: ga4gh, hpo, monarchinitiative, phenopackets
- Language: Jupyter Notebook
- Homepage: https://monarch-initiative.github.io/phenopacket-store/
- Size: 33.8 MB
- Stars: 26
- Watchers: 5
- Forks: 5
- Open Issues: 19
-
Metadata Files:
- Readme: README.md
- Contributing: docs/contributing.md
- License: LICENSE
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README
# phenopacket store
[](https://zenodo.org/doi/10.5281/zenodo.13168726)
This repository offers cohorts of [GA4GH Phenopackets](https://phenopacket-schema.readthedocs.io/en/latest/) that
represent individuals with Mendelian diseases,
as described in [Danis *et al*, HGG Advances 2025](https://www.cell.com/hgg-advances/fulltext/S2666-2477(24)00111-8).
Please see the [online documentation](https://monarch-initiative.github.io/phenopacket-store) for information about available cohorts.
## Availability
Phenopacket store releases are available for download from the
[Releases](https://github.com/monarch-initiative/phenopacket-store/releases) section.
The *latest* release ZIP archive is available for download from a stable URL:
**ZIP**
```
https://github.com/monarch-initiative/phenopacket-store/releases/latest/download/all_phenopackets.zip
```
## Python support
We provide special support for Python with [Phenopacket Store Toolkit](https://github.com/monarch-initiative/phenopacket-store-toolkit)
to simplify accessing the Phenopacket Store data in downstream applications.
The toolkit is available at [Python Package Index](https://pypi.org/project/phenopacket-store-toolkit/) (PyPi)
and can be installed, e.g. with `pip`:
```shell
python3 -m pip install phenopacket-store-toolkit
```
After installation, loading phenopackets from Phenopacket Store is super easy.
First, we create Phenopacket Store registry, an object for managing local data files
of Phenopacket Store releases:
```python
from ppktstore.registry import configure_phenopacket_registry
registry = configure_phenopacket_registry()
```
By default, the `registry` keeps the files in data directory at `$HOME/.phenopacket-store` (or similar on Windows), but this can be configured if desired.
Then, we can use `registry` to load phenopackets of a cohort, e.g. *SUOX* of release `0.1.18`:
```python
with registry.open_phenopacket_store(release="0.1.18") as ps:
phenopackets = list(ps.iter_cohort_phenopackets("SUOX"))
assert len(phenopackets) == 35
```
The registry peeks into the data directory to check if the `0.1.18` release ZIP file has already been downloaded.
If absent, the registry will download the ZIP file from Github. Then, we open Phenopacket Store as `ps` and we load 35 phenopackets of *SUOX* cohort.
More info about Phenopacket Store Toolkit is available
in its [documentation](https://monarch-initiative.github.io/phenopacket-store-toolkit/stable).
## Contributing
The cohorts were curated from data medical publications, mainly by parsing the tables or supplemental tables.
The curation was done in Jupyter notebooks using [pyphetools](https://pypi.org/project/pyphetools/) library.
Pull requests with additional notebooks in the same style are welcome.
## Citing Phenopacket Store
If you use Phenopacket Store in a scientific publication, we would appreciate citations to the following paper:
[A corpus of GA4GH phenopackets: Case-level phenotyping for genomic diagnostics and discovery](https://www.cell.com/hgg-advances/fulltext/S2666-2477(24)00111-8), Danis et al., Human Genetics and Genomics Advances, Volume 6, Issue 1, 100371