Documentation: https://mjvolk3.github.io/torchcell/
Database: https://torchcell-database.ncsa.illinois.edu:7473/browser/ (Neo4j Browser over the served TorchCell knowledge graph)
Database releases and compatibility: https://mjvolk3.github.io/torchcell/database/compatibility.html
Datasets: https://mjvolk3.github.io/torchcell/datasets/index.html
Dataset downloads (the tc-data API): https://mjvolk3.github.io/torchcell/guide/downloads.html
Download a dataset
Built datasets are served as versioned archives (the records LMDB plus its build
manifest) by the tc-data endpoint on the database host, with Swagger at /docs. Each
page below is one supported query over the knowledge graph and bundles the datasets it
returns: what the experiments measured, one stored record each, the value distributions,
the query, and the download commands.
| Page | Datasets (records) |
|---|---|
| Gene essentiality and single-mutant fitness | Gene essentiality, SGD (1,329) Single-mutant fitness, Costanzo 2016 (20,484) |
| Amino acids and betaxanthin | Amino acids, Mulleder 2016 (4,678) Amine peaks, Cooper 2010 (4,313) Betaxanthin, Cachera 2023 (4,719) |
With an endpoint URL and a key (see Downloading datasets), a loader fetches its archive instead of building. Export the two variables in the shell:
export TC_DATA_URL=http://torchcell-database.ncsa.illinois.edu:8724
export TC_DATA_API_KEY=<your key>
or put the same two lines, without export, in a .env file in your project and load
it before constructing a loader:
from dotenv import load_dotenv
load_dotenv() # reads the .env beside this script or in a parent directory
from torchcell.datasets.scerevisiae.mulleder2016 import AminoAcidMulleder2016Dataset
dataset = AminoAcidMulleder2016Dataset(root="data/torchcell/amino_acid_mulleder2016")
The loader reads the two variables from the process environment and does not read .env
itself. With TC_DATA_URL unset it builds from the publisher's files instead.
The full collection served by the knowledge graph (51 datasets, 52.7 million experiments at release 2026.09.21) is listed on the Datasets page.
1 maintainer
Metadata
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Release files for torchcell 1.7.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| torchcell-1.7.4.tar.gz | 4.6 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| torchcell-1.7.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 9.8 MB
Release files / torchcell-1.7.4.tar.gz
| Download URL | torchcell-1.7.4.tar.gz |
|---|---|
| Size | 4.6 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
bdc00038f240ea2fb66a08cda9ee7906ba5dead3aa5fa1689dad7f91fd7fd4a7
|
|
BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
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Signed by GitHub Actions, verified by PyPI on Oct 14, 2026.
Transparency logRelease files / torchcell-1.7.4-py3-none-any.whl
| Download URL | torchcell-1.7.4-py3-none-any.whl |
|---|---|
| Size | 5.2 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
10b7cb99c905f6064b8025d678b316c29e1642254568088729440bb251293216
|
|
BLAKE2b-256 checksum How to use checksums |
739e3fcd31b1a8031cfae7a2bffbc4ef4b40c9b02054eb1926d619b76dd3ab0f
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 14, 2026.
Transparency log
