onsite
onsite 🔬🎯
🚀 What is onsite?
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).
✨ Key Features
- 🎯 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
📊 Benchmark
We benchmarked onsite algorithms on the PXD000138 dataset using unified mzML/idXML inputs and consistent filtering (FDR < 0.01, tool-specific localization thresholds). Key takeaways:
| Tool | Total PSMs | Total phospho sites | Well-resolved sites | Uncertain sites | Global FLR |
|---|---|---|---|---|---|
| LuciPHOr | 111,588 | 118,625 | 48,186 | 37,337 | 0.0237 |
| AScore | 111,747 | 101,382 | 52,906 | 23,628 | 0.0283 |
| pyLucXor | 111,588 | 117,341 | 51,468 | 38,970 | 0.0304 |
| PhosphoRS | 111,747 | 107,552 | 50,000 | 26,354 | 0.0142 |
See benchmark.md for methodology, full tables, and analysis details.
📋 Supported Algorithms
onsite provides three complementary algorithms for PTM localization:
1. AScore Algorithm
- 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
2. PhosphoRS Algorithm
- 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
3. LucXor (LuciPHOr2) Algorithm
- Method: Two-stage processing with FLR estimation
- Features: False localization rate calculation, decoy-based validation
- Output: Delta scores, peptide scores, global and local FLR
- Citation: Fermin et al. (2013, 2015) MCP and Bioinformatics
💾 Installation
Prerequisites
- Python 3.11+
- PyOpenMS 3.4.0+
- NumPy 2.3.2+
- SciPy 1.16.1+
Using Poetry (Recommended)
# Clone the repository
git clone https://github.com/bigbio/onsite.git
cd onsite
# Install with Poetry
poetry install
# Activate the virtual environment
poetry shell
Using pip
# Install from PyPI (when available)
pip install onsite
# Or install from source
git clone https://github.com/bigbio/onsite.git
cd onsite
pip install -e .
Development Installation
# 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]"
🛠️ Usage
Command Line Interface
onsite provides a unified command-line interface for all algorithms:
Unified onsite CLI
# 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
Individual Pipeline Tools
AScore Pipeline
# 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
PhosphoRS Pipeline
# 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
LucXor Pipeline
# 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
Command-line Options
AScore Options
| 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 |
--debug |
False | Enable debug logging |
PhosphoRS Options
| 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 |
--debug |
False | Enable debug logging |
LucXor Options
| 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) |
--min-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 |
--debug |
False | Enable debug logging |
📊 Algorithm Details
AScore Algorithm
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
PhosphoRS Algorithm
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 (LuciPHOr2) Algorithm
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
🔍 Example Results
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 |
📖 Documentation
For more detailed information:
- AScore Algorithm Documentation
- PhosphoRS Algorithm Documentation
- LucXor Algorithm Documentation
- Citations and References
👥 Contributing
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
📜 License
This project is licensed under the MIT License - see the LICENSE file for details.
📝 Citation
If you use onsite in your research, please cite:
onsite: Mass spectrometry post-translational modification localization tool.
https://github.com/bigbio/onsite
🔗 Related Tools
- PyOpenMS - Python bindings for OpenMS
- OpenMS - Open-source tools for mass spectrometry
- nf-core/quantms - Quantitative mass spectrometry workflow
❓ Need Help?
If you have questions or need assistance:
- Open an issue on GitHub
- Check existing issues for solutions
🙏 Acknowledgments
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.