mzIdentML-polars
A fast Rust-based writer for mzIdentML 1.3 files using Polars DataFrames as input. This project simplifies the generation of standard-compliant proteomics identification files, with built-in support for:
- Polars Integration: Directly write mzIdentML from high-performance DataFrames.
- ProForma v2: Support for standard peptide sequence notation (e.g.,
PEPT[Unimod:35]IDEK). - Crosslinking: Native encoding for crosslinked peptide matches (CSMs).
- mzIdentML 1.3.0 Compliance: Generates valid XML according to the latest PSI-PI standards.
Installation
You can install the Python bindings directly from the source using maturin:
# Clone the repository
git clone https://github.com/Rappsilber-Laboratory/mzIdentML-polars.git
cd mzIdentML-polars
# Install via pipenv (requires a Rust toolchain and maturin)
pipenv install
pipenv run maturin develop
Usage
The primary functions are write_mzidentml (for file output) and serialize_mzidentml (for string output). Both take Polars DataFrames and a dictionary for metadata. Note that write_mzidentml takes the output path as its first argument.
Writing to a File (Recommended)
This method is memory-efficient as it streams the XML directly to the disk. It also supports automatic Gzip compression if the filename ends in .gz.
import polars as pl
import mzidentml_polars
# ... define DataFrames ...
# Generate mzIdentML directly to a file
mzidentml_polars.write_mzidentml("output.mzid", csms, prot_seqs, spectra, metadata)
# Automatic Gzip compression
mzidentml_polars.write_mzidentml("output.mzid.gz", csms, prot_seqs, spectra, metadata)
Serializing to a String
# Generate mzIdentML as a string (if needed for further processing)
xml_string = mzidentml_polars.serialize_mzidentml(csms, prot_seqs, spectra, metadata)
Testing
The project includes a comprehensive test suite using pytest that validates output against official mzIdentML XML schemas.
Prerequisites
pip install pytest lxml
Running Tests
pipenv run pytest tests/
Troubleshooting
TypeError: ... compat_level has invalid type: 'int'
If you see this error, it indicates a version mismatch between your Python polars and the polars Rust crate used during compilation.
The build process now automatically synchronizes these versions by updating pyproject.toml based on Cargo.toml. If you encounter this after manual dependency changes, simply rebuild the project:
pipenv run maturin develop
This will ensure your Python environment matches the compiled extension's expected ABI.
No module named 'pyarrow'
pyo3-polars may require pyarrow for internal data conversions:
pip install pyarrow
Input Schemas
prot_seqs (DataFrame)
| Column | Type | Description |
|---|---|---|
protein_id |
String | Unique internal ID for the protein |
accession |
String | Public accession (e.g., UniProt) |
protein_name |
String | Optional. Descriptive name for the protein (e.g., BIPA_BACSU) |
sequence |
String | Full amino acid sequence |
is_decoy |
Boolean | Whether the protein is a decoy (default: false) |
csms (DataFrame)
| Column | Type | Description |
|---|---|---|
spectrum_id |
String | ID of the spectrum (e.g., index=1 or scan=123) |
file_path |
String | Path to the source file to resolve duplicate IDs across files. |
peptide1_seq |
String | ProForma v2 sequence of the first peptide |
protein1_id |
String / List[Str] | ID matching prot_seqs |
peptide1_start |
UInt32 / List[U32] | Start position in protein (1-based) |
peptide1_end |
UInt32 / List[U32] | End position in protein (1-based) |
charge |
Int32 | Precursor charge state |
rank |
UInt32 | Identification rank (1 = top match) |
is_crosslink |
Boolean | Whether this is a crosslink match |
is_looplink |
Boolean | Whether this is a looplink match |
experimental_mz |
Float64 | Recommended. Observed precursor m/z |
calculated_mz |
Float64 | Recommended. Theoretical precursor m/z |
score |
Float64 | Recommended. Primary search engine score |
peptide1_link_pos |
Int32 | 1-based link position on peptide 1 |
peptide2_link_pos |
Int32 | 1-based link position on peptide 2 (or site 2 for looplink) |
peptide2_seq |
String | (Crosslink only) Second peptide sequence |
protein2_id |
String / List[Str] | (Crosslink only) Second protein ID |
peptide2_start |
UInt32 / List[U32] | (Crosslink only) Start position |
peptide2_end |
UInt32 / List[U32] | (Crosslink only) End position |
crosslinker_name |
String | Recommended. Name of the crosslinker (e.g., DSSO) |
crosslinker_accession |
String | Recommended. CV accession of the crosslinker (e.g., MS:1003124) |
crosslinker_mass |
Float64 | Recommended. Mass of the crosslinker |
metadata (Dictionary)
| Key | Type | Description |
|---|---|---|
software_name |
String | Name of the analysis software (default: mzidentml-polars) |
software_version |
String | Version of the software |
author |
String | Name of the primary researcher/author |
is_ppm |
Boolean | Whether tolerances are in PPM (default: true) |
parent_plus |
Float | Precursor tolerance upper bound |
parent_minus |
Float | Precursor tolerance lower bound |
frag_plus |
Float | Fragment tolerance upper bound |
frag_minus |
Float | Fragment tolerance lower bound |
enzymes |
List[Dict] | Enzymes used: [{"name": "Trypsin", "accession": "MS:1001251"}] |
modifications |
List[Dict] | Search mods: [{"fixed": true, "mass": 57.02, "residues": "C", "name": "Carbamidomethyl", "accession": "UNIMOD:4"}] |
search_params |
List[Dict] | Additional parameters: [{"name": "xi:min_peptide_length", "accession": "MS:1002543", "value": "6"}] |
Protein Ambiguity
If a peptide sequence maps to multiple proteins, you can encode this using Polars List columns in the csms DataFrame. For each mapped protein, provide the corresponding ID, start, and end positions in the lists. The library will generate multiple <PeptideEvidence> entries for that match.
csms = pl.DataFrame({
"protein1_id": [["PROT_A", "PROT_B"], ["PROT_C"]],
"peptide1_start": [[1, 50], [10]],
"peptide1_end": [[10, 60], [20]],
# ... other columns
})
Development & Releases
Version Management
This project uses Git tags as the single source of truth for versioning.
- Python: Managed by
setuptools_scm. The version is automatically derived from the latest Git tag (e.g.,v0.1.0). If no tag is present, it defaults to a.devversion. - Rust: The version is hardcoded in
Cargo.toml. To ensure consistency, always usecargo-releaseto bump versions.
Bumping the Version
To create a new release (e.g., moving from 0.1.0 to 0.2.0):
-
Install cargo-release:
cargo install cargo-release
-
Verify changes (Dry Run):
cargo release minor --no-publish
-
Perform the Release:
# Bumps version, commits, tags, and pushes cargo release minor --execute --no-publish
Note:
--no-publishskips publishing to crates.io; the GitHub Action handles PyPI publishing via tags. -
CI/CD: The GitHub Action (
.github/workflows/pypi.yml) will automatically trigger on the new tag and publish the updated wheels to PyPI.
Syncing Polars
If you change the polars version in Cargo.toml, the build script (build.rs) will automatically run sync_polars.py to update the constraints in pyproject.toml.
License
This project is licensed under the Apache-2.0 License.
TODO
- Implementing basic Protein Grouping (ProteinDetectionList) support, even as a simple 1-to-1 mapping if full inference isn't required.
- Write passThreshold column
2 maintainers
Metadata
2 maintainers
Release files for mzidentml-polars 0.1.3
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