mzidentml-polars 0.1.3

A fast Rust-based writer for mzIdentML 1.3 files using Polars

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 .dev version.
• Rust: The version is hardcoded in Cargo.toml. To ensure consistency, always use cargo-release to bump versions.

Bumping the Version

To create a new release (e.g., moving from 0.1.0 to 0.2.0):

1. Install cargo-release:

   cargo install cargo-release
   

2. Verify changes (Dry Run):

   cargo release minor --no-publish
   

3. Perform the Release:

   # Bumps version, commits, tags, and pushes
   cargo release minor --execute --no-publish
   

   Note: --no-publish skips publishing to crates.io; the GitHub Action handles PyPI publishing via tags.

4. 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