tsarina
Tsarina selects shared cancer immunotherapy targets for an individual patient or a population HLA panel.
It starts with reusable cancer-testis antigen (CTA), oncogenic-virus, and recurrent-mutation targets. It then combines tumor context, public immunopeptidomics evidence, healthy-tissue safety evidence, and predicted HLA presentation to produce ranked peptide-MHC (pMHC) candidates.
Choose a workflow
| Goal | Entry point | Result |
|---|---|---|
| Prioritize targets for one patient | tsarina personalize or personalized_targets() |
Ranked CTA, viral, and mutant pMHCs for that patient's HLA type and tumor |
| Design an off-the-shelf CTA panel | tsarina panel |
CTA × HLA matrix with evidence tiers and population-coverage estimates |
| Assemble a shared CTA vaccine | tsarina vaccine |
Mortality-ranked proteoforms, sequence funnel, junction audit and DNA/RNA construct |
| Inspect public peptide observations | tsarina hits |
Cancer, healthy-tissue, and restriction evidence for specified peptides |
Start with the documentation guide for inputs, data setup, and the workflow-specific guides.
Install
pip install tsarina
Install the optional peptide-generation and partitioning dependencies for full functionality:
pip install "tsarina[all]"
Quick start
Prioritize targets for a patient:
tsarina personalize \
--hla 'HLA-A*02:01,HLA-A*24:02,HLA-B*07:02' \
--cta 'MAGEA4=142.5,PRAME=87.3' \
--mutations "KRAS G12D" \
--viruses hpv16 \
--output patient-targets.csv
Build the default global CTA × HLA panel:
tsarina panel --format long --output panel.csv
Both workflows can use registered IEDB or CEDAR ligand exports as public immunopeptidomics evidence. See Data and evidence for setup and interpretation.
Target categories
| Category | Candidate source | Tumor-specific context |
|---|---|---|
| CTA | The canonical oncoref CTA set | Tumor RNA expression and reproductive-tissue restriction |
| Viral | Proteomes from nine oncogenic viruses | Virus detected in the tumor |
| Mutant | Nineteen recurrent hotspots across seven driver genes | Matching mutation detected in the tumor |
Oncoref is the single authority for CTA membership, aliases, HPA restriction calls, and proteoform groups. Tsarina adds downstream evidence and scoring without maintaining a second CTA definition library. The exact boundary is documented in CTA ownership and downstream evidence.
How ranking works
Tsarina applies the same high-level sequence across workflows:
- choose candidates from the relevant shared-target sets;
- enforce tumor context and exclude non-target human peptide matches;
- annotate public cancer and healthy-tissue MS observations;
- predict presentation by the requested HLA alleles; and
- rank pMHCs with explicit evidence and safety provenance.
Public MS evidence strengthens a candidate but does not redefine CTA membership. Healthy, direct-ex-vivo observations outside reproductive tissues and thymus are treated as safety evidence.
Documentation
- Documentation guide — workflow selection and shared concepts
- Personalized target selection — patient inputs, prioritization, and output schema
- CTA panel design — automatic selection, evidence tiers, HLA panels, and coverage
- CTA vaccine design — mortality-weighted p95 selection, strict/loose specificity, MS-supported stretches, construct search and full audit
- Data and evidence — data registry, observation classification, scoring, and naming
- CTA ownership and downstream evidence — oncoref/Tsarina responsibilities and maintenance
Development
./develop.sh
./format.sh
./lint.sh
./test.sh
develop.sh uses the active virtualenv (or creates .venv) and requires
pip >=23.0. It resolves Tsarina and present sibling checkouts together, checks
the proposed versions against installed consumers before changing packages,
and runs pip check after installation. If a checkout is incompatible, update
that checkout or use a separate virtualenv; the refused install leaves the
installed packages unchanged. Missing siblings use their released packages.
Real MHCflurry model integration tests run in a separate CI job. To run them locally (Python 3.10+):
pip install -e ".[test-mhcflurry]"
mhcflurry downloads fetch models_class1_presentation
pytest tests/test_mhcflurry_integration.py --run-mhcflurry
These tests check the downloaded presentation bundle, including its affinity
calibration, through Tsarina's scoring interface. Enabling them requires the
dependency and models; missing prerequisites fail rather than skip. Use
./test.sh --run-mhcflurry to include them in the full suite.
./deploy.sh requires this extra and model bundle and always runs these tests.
1 maintainer
Metadata
1 maintainer
Release files for tsarina 1.34.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| tsarina-1.34.1.tar.gz | 2.1 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| tsarina-1.34.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 2.7 MB
Release files / tsarina-1.34.1.tar.gz
| Download URL | tsarina-1.34.1.tar.gz |
|---|---|
| Size | 2.1 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
5e6f024a568ddd8a40325f8f0b80f4dd6e944d73b60f686279024c2078813433
|
|
BLAKE2b-256 checksum How to use checksums |
314c435dcfd590e42d5e0db82a66698828b5072aec7f5f1507a9fde9cc1d7dfc
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.6
|
Release files / tsarina-1.34.1-py3-none-any.whl
| Download URL | tsarina-1.34.1-py3-none-any.whl |
|---|---|
| Size | 652.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
773f790811c095b1a612177656e8c073584552dfd580535492fe43b239055ce0
|
|
BLAKE2b-256 checksum How to use checksums |
27167bd9ea1da2e6829d0187453c1fa28922791458b05554f9c86efc5ed85e59
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.6
|
