onsite is a comprehensive Python package for mass spectrometry post-translational modification (PTM) localization. It provides algorithms for confident phosphorylation site localization and scoring, including implementations of AScore, PhosphoRS, and LucXor (LuciPHOr2).
- Multiple Algorithms: AScore, PhosphoRS, and LucXor implementations
- Statistical Validation: Probability-based scoring with FLR estimation
- Unified CLI: Single command-line interface for all algorithms
- Multi-threading: Parallel processing for improved performance
- PyOpenMS Integration: Seamless integration with the OpenMS ecosystem
- High Accuracy: Confident site localization with statistical validation
- Flexible API: Both command-line and Python API support
onsite provides three complementary algorithms for PTM localization:
- Method: Probability-based approach using binomial statistics
- Features: Site-determining ion analysis, fast processing
- Output: AScore values indicating localization confidence
- Citation: Beausoleil et al. (2006) Nature Biotechnology
- Method: Compomics-style scoring with isomer analysis
- Features: Site-specific probabilities, detailed isomer analysis
- Output: Site probability scores and isomer details
- Citation: Taus et al. (2011) Journal of Proteome Research
- Method: Two-stage processing with FLR estimation
- Features: False localization rate calculation, decoy-bas 5CC0 ed validation
- Output: Delta scores, peptide scores, global and local FLR
- Citation: Fermin et al. (2013, 2015) MCP and Bioinformatics
- Python 3.11+
- PyOpenMS 3.5.0+
- NumPy 2.3.2+
- SciPy 1.16.1+
# Clone the repository
git clone https://github.com/bigbio/onsite.git
cd onsite
# Install with Poetry
poetry install
# Activate the virtual environment
poetry shell# Install from PyPI (note: PyPI package name is 'pyonsite')
pip install pyonsite
# Or install from source
git clone https://github.com/bigbio/onsite.git
cd onsite
pip install -e .Note: The package is published on PyPI as pyonsite due to a naming conflict, but the module is still imported as onsite.
# Clone the repository
git clone https://github.com/bigbio/onsite.git
cd onsite
# Install with development dependencies
poetry install --with dev
# Or with pip
pip install -e ".[dev]"onsite provides a unified command-line interface for all algorithms:
# AScore algorithm
onsite ascore -in spectra.mzML -id identifications.idXML -out results.idXML
# PhosphoRS algorithm
onsite phosphors -in spectra.mzML -id identifications.idXML -out results.idXML
# LucXor algorithm
onsite lucxor -in spectra.mzML -id identifications.idXML -out results.idXML# Basic usage
python -m onsite.ascore.cli -in spectra.mzML -id identifications.idXML -out results.idXML
# With custom parameters
python -m onsite.ascore.cli -in spectra.mzML -id identifications.idXML -out results.idXML \
--fragment-mass-tolerance 0.05 \
--fragment-mass-unit Da \
--threads 4 \
--add-decoys# Basic usage
python -m onsite.phosphors.cli -in spectra.mzML -id identifications.idXML -out results.idXML
# With custom parameters
python -m onsite.phosphors.cli -in spectra.mzML -id identifications.idXML -out results.idXML \
--fragment-mass-tolerance 0.05 \
--fragment-mass-unit Da \
--threads 1 \
--add-decoys# Basic usage
python -m onsite.lucxor.cli -in spectra.mzML -id identifications.idXML -out results.idXML
# With custom parameters
python -m onsite.lucxor.cli -in spectra.mzML -id identifications.idXML -out results.idXML \
--fragment-method HCD \
--fragment-mass-tolerance 0.5 \
--fragment-error-units Da \
--threads 8 \
--debug| Option | Default | Description |
|---|---|---|
-in |
- | Input mzML file with spectra |
-id |
- | Input idXML file with identifications |
-out |
- | Output idXML file with scores |
--fragment-mass-tolerance |
0.05 | Fragment mass tolerance |
--fragment-mass-unit |
Da | Tolerance unit (Da or ppm) |
--threads |
1 | Number of threads for parallel processing |
--add-decoys |
False | Include decoy sites for validation |
--compute-all-scores |
False | Run all three algorithms and merge results |
--debug |
False | Enable debug logging |
| Option | Default | Description |
|---|---|---|
-in |
- | Input mzML file with spectra |
-id |
- | Input idXML file with identifications |
-out |
- | Output idXML file with scores |
--fragment-mass-tolerance |
0.05 | Fragment mass tolerance |
--fragment-mass-unit |
Da | Tolerance unit (Da or ppm) |
--threads |
1 | Number of threads for parallel processing |
--add-decoys |
False | Include decoy sites for validation |
--compute-all-scores |
False | Run all three algorithms and merge results |
--debug |
False | Enable debug logging |
| Option | Default | Description |
|---|---|---|
-in |
- | Input mzML file with spectra |
-id |
- | Input idXML file with identifications |
-out |
- | Output idXML file with scores |
--fragment-method |
CID | Fragmentation method (CID or HCD) |
--fragment-mass-tolerance |
0.5 | Fragment mass tolerance |
--fragment-error-units |
Da | Tolerance units (Da or ppm) |
--m
A60A
in-mz |
150.0 | Minimum m/z value to consider |
--target-modifications |
Phospho (S/T/Y) | List of target PTM definitions |
--neutral-losses |
sty -H3PO4 -97.97690 | Neutral loss definitions applied during scoring |
--decoy-mass |
79.966331 | Mass offset used when generating decoy permutations |
--decoy-neutral-losses |
X -H3PO4 -97.97690 | Neutral loss patterns for decoy permutations |
--max-charge-state |
5 | Maximum charge state |
--max-peptide-length |
40 | Maximum peptide length |
--max-num-perm |
16384 | Maximum permutations |
--modeling-score-threshold |
0.95 | Minimum score for selecting PSMs during model building |
--scoring-threshold |
0.0 | Minimum LucXor score to report |
--min-num-psms-model |
50 | Minimum number of high-scoring PSMs required for modeling |
--threads |
1 | Number of threads for parallel processing |
--rt-tolerance |
0.01 | RT tolerance used when matching spectra by retention time |
--disable-split-by-charge |
False | Disable splitting PSMs by charge state for model training |
--compute-all-scores |
False | Run all three algorithms and merge results |
--debug |
False | Enable debug logging |
The AScore algorithm provides phosphorylation site localization by analyzing MS/MS fragment ions to identify site-determining ions and computing localization probabilities based on fragment evidence.
Output Metrics:
AScore_pep_score: Overall peptide scoreAScore_1, AScore_2, ...: Individual site scoresProForma: Standardized sequence notation with confidence scores
The PhosphoRS algorithm implements a comprehensive approach using isomer generation, theoretical spectrum matching, and probability scoring for confident phosphorylation site assignment.
Output Metrics:
- Site-specific probability scores (0-100%)
- Isomer details with sequence and score
- Detailed confidence metrics
LucXor implements the complete LuciPHOr2 algorithm with two-stage processing for accurate PTM localization with false localization rate (FLR) estimation.
Output Metrics:
Luciphor_delta_score: Main localization scoreLuciphor_pep_score: Peptide identification scoreLuciphor_global_flr: Global false localization rateLuciphor_local_flr: Local false localization rate
You can find example result files in the data directory. Here are the direct links to different algorithm result files:
| Algorithm | Description | Result File |
|---|---|---|
| AScore | AScore phosphorylation site localization results | AScore Example |
| PhosphoRS | PhosphoRS phosphorylation site localization results | PhosphoRS Example |
| LucXor | LucXor (LuciPHOr2) PTM localization results with FLR | LucXor Example |
For more detailed information:
- AScore Algorithm Documentation
- PhosphoRS Algorithm Documentation
- LucXor Algorithm Documentation
- Citations and References
To contribute to onsite:
- Fork the repository
- Clone your fork:
git clone https://github.com/YOUR-USERNAME/onsite - Create a feature branch:
git checkout -b new-feature - Make your changes
- Install in development mode:
pip install -e . - Test your changes:
poetry run pytest - Commit your changes:
git commit -am 'Add new feature' - Push to the branch:
git push origin new-feature - Submit a pull request
This project is licensed under the MIT License - see the LICENSE file for details.
If you use onsite in your research, please cite:
onsite: Mass spectrometry post-translational modification localization tool.
https://github.com/bigbio/onsite
- PyOpenMS - Python bindings for OpenMS
- OpenMS - Open-source tools for mass spectrometry
- nf-core/quantms - Quantitative mass spectrometry workflow
If you have questions or need assistance:
- Open an issue on GitHub
- Check existing issues for solutions
onsite builds upon the excellent work of the original algorithm developers and the OpenMS community. We thank all contributors and users for their feedback and support.