quantms-rescoring 0.0.13

quantms-rescoring: Python scripts and helpers for the quantMS workflow

quantms-rescoring

quantms-rescoring is a Python tool that aims to add features to peptide-spectrum matches (PSMs) in idXML files using multiple tools including SAGE features, quantms spectrum features, MS2PIP, AlphaPeptDeep and DeepLC. It is part of the quantms ecosystem package and leverages the MS²Rescore framework to improve identification confidence in proteomics data analysis.

Core Components

• Annotator Engine: Integrates MS2PIP, AlphaPeptDeep and DeepLC models to improve peptide-spectrum match (PSM) confidence.
• Feature Generation: Extracts signal-to-noise ratios, spectrum metrics, SAGE extra features and add them to each PSM for posterior downstream with Percolator.
• OpenMS Integration: Processes idXML and mzML files with custom validation methods.
• Transfer learning: Implemented train and fine-tune the ms2 model to generate project-specific model and pass the new model to quantms workflow for rescoring.

CLI Tools

 rescoring msrescore2feature --help

Annotates PSMs with prediction-based features from MS2PIP and DeepLC

 rescoring add_sage_feature --help

Incorporates additional features from SAGE into idXML files.

 rescoring spectrum2feature --help

Add additional spectrum feature like signal-to-noise to each PSM in the idXML.

 rescoring psm_feature_clean --help

Check and clean invalid PSM with invalid features in the idXML.

 rescoring download_models --help

Download all required models (MS2PIP, AlphaPeptDeep) for offline use. This is useful for running quantms-rescoring in environments without internet access, such as HPC clusters.

 rescoring transfer_learning --help

Perform train and fine-tuning model for AlphaPeptDeep to generate project-specific model. This is useful for running quantms-rescoring in PTM datasets, which are unseen for AlphaPeptDeep pretrained model.

Advanced Algorithms and Improvements

quantms-rescoring significantly enhances the capabilities of MS2PIP, AlphaPeptDeep, DeepLC, and MS2Rescore through several innovative approaches:

MS2PIP Integration Enhancements

• Intelligent Model Selection: Automatically evaluates and selects the optimal MS2PIP model for each dataset based on fragmentation type and correlation quality. If the user-selected model performs poorly, the system will intelligently search for a better alternative.
• Adaptive MS2 Tolerance: Dynamically adjusts MS2 tolerance based on the dataset characteristics, analyzing both reported and predicted tolerances to find the optimal setting.
• Correlation Validation: Implements a robust validation system that ensures the selected model achieves sufficient correlation with experimental spectra, preventing the use of inappropriate models.
• Enhanced Spectrum Processing: Uses OpenMS for spectrum file reading instead of ms2rescore_rs, providing better compatibility with a wider range of mzML files and formats.

AlphaPeptDeep Innovations

• Fine-tuning: Leverages fine-tuning to adapt models to specific experimental/project conditions based on identifications file (idXML) from quantms, improving prediction accuracy for challenging datasets, such as PTM.
• Model Optimization: Automatically benchmarks pretrained vs. retrained AlphaPeptDeep models for each dataset, selecting the one with the better Median PCC for MS2 intensity prediction.
• Enhanced Spectrum Processing: AlphaPeptDeep does not support idXML input, so we use OpenMS for spectrum file reading and pass it to AlphaPeptDeep for prediction and fine-tuning.
• Correlation Validation: Implements a robust validation system that ensures the pretrained and retrained models achieve sufficient correlation with experimental spectra, preventing the use of inappropriate models.

DeepLC Innovations

• Model Optimization: Automatically benchmarks pretrained vs. retrained DeepLC models for each dataset, selecting the one with the lowest Mean Absolute Error (MAE) for retention time prediction.
• Per-Run Calibration: Calibrates DeepLC models for each run to account for chromatographic variations between experiments, improving prediction accuracy.
• Best Peptide Retention Time: Tracks the best retention time prediction for each peptide across multiple PSMs, providing more reliable retention time features.
• Transfer Learning: Leverages transfer learning to adapt models to specific experimental conditions, improving prediction accuracy for challenging datasets.

Spectrum Feature Analysis

Unlike traditional rescoring approaches, quantms-rescoring incorporates advanced spectrum quality metrics:

• Signal-to-Noise Ratio (SNR): Calculates the ratio of maximum intensity to background noise, providing a robust measure of spectrum quality.
• Spectral Entropy: Quantifies the uniformity of peak distribution, helping to distinguish between high and low-quality spectra.
• TIC Distribution Analysis: Analyzes the distribution of Total Ion Current across peaks, identifying spectra with concentrated signal in top peaks.
• Weighted m/z Standard Deviation: Estimates spectral complexity by calculating the intensity-weighted standard deviation of m/z values.

SAGE Feature Integration

• Seamless Integration: Incorporates additional features from SAGE (Spectrum Agnostic Generation of Embeddings) into the rescoring pipeline.
• Feature Validation: Ensures all features are properly validated and formatted for compatibility with OpenMS and downstream tools.

Advantages Over Existing Tools

• Compared to MS2PIP: Adds automatic model selection, validation, features calculations and tolerance optimization, eliminating the need for manual parameter tuning.
• Compared to DeepLC: Provides automatic model selection between pretrained and retrained models, with per-run calibration for improved accuracy.
• Compared to MS2Rescore: Integrates a broader range of MS2 prediction models, including AlphaPeptDeep, and supports fine-tuning to generate project-specific models. It provides a richer feature set encompassing spectrum quality metrics, tighter integration with OpenMS, and more robust support for diverse fragmentation methods and MS levels.
• Compared to AlphaPeptDeep: Seamlessly integrates into the quantms workflow and natively supports the quantms identification results format. It introduces automatic model selection and validation, delivers an expanded feature set, and offers improved handling of different fragmentation methods and MS levels.

Technical Implementation Details

Model Selection and Optimization

• MS2PIP Model Selection:
  • Automatically evaluate the quality of the MS2PIP model selected by the user. If the correlation between predicted and experimental spectra is lower than a given threshold, we will try to find the best model to use (annotator.py). For example, if the user provides as model parameter HCD for a CI experiment, the tool will try to find the best model for this experiment within the CID models available.
  • If the ms_tolerance is to restrictive for the data (e.g. 0.05 Da for a 0.5 Da dataset), the tool will try to find the annotated tolerances in the idXML file and use the best model for this tolerance.
• AlphaPeptDeep Model:
  • Automatically evaluate the quality of the AlphaPeptDeep model weight passed by the user. If the correlation between predicted and experimental spectra is lower than a given threshold, we will skip MS2 features generation to avoid potential erroneous results.
  • When enabling transfer_learning, the tool will try to fine-tune the AlphaPeptDeep model on the given idXML and mzML files and compare it with the pretrained model, finally using the best model based on similarity metrics.
• DeepLC Model Selection:
  • Automatically select the best DeepLC model for each run based on the retention time calibration and prediction accuracy. Different to ms2rescore, the tool will try to use the best model from MS2PIP and benchmark it with the same model by using transfer learning (annotator.py). The best model is selected to be used to predict the retention time of PSMs.

Feature Engineering Pipeline

• Retention Time Analysis:

  • Calibrates DeepLC models per run to account for chromatographic variations.
  • Calculates delta RT (predicted vs. observed) as a discriminative feature
  • Normalizes RT differences for cross-run comparability

• Spectral Feature Extraction:

  • Computes signal-to-noise ratio using maximum intensity relative to background noise
  • Calculates spectral entropy to quantify peak distribution uniformity
  • Analyzes TIC (Total Ion Current) distribution across peaks for quality assessment
  • Determines weighted standard deviation of m/z values for spectral complexity estimation

• Feature Selection: The parameters only_features allows to select the features to be added to the idXML file. For example: --only_features "DeepLC:RtDiff,DeepLC:PredictedRetentionTimeBest,Ms2pip:DotProd".

Features

MS2PIP and AlphaPeptDeep Feature Mapping Table

MS2PIP and AlphaPeptDeep Feature	quantms-rescoring Name
spec_pearson	MS2PIP/AlphaPeptDeep:SpecPearson
cos_norm	MS2PIP/AlphaPeptDeep:SpecCosineNorm
spec_pearson_norm	MS2PIP/AlphaPeptDeep:SpecPearsonNorm
dotprod	MS2PIP/AlphaPeptDeep:DotProd
ionb_pearson_norm	MS2PIP/AlphaPeptDeep:IonBPearsonNorm
iony_pearson_norm	MS2PIP/AlphaPeptDeep:IonYPearsonNorm
spec_mse_norm	MS2PIP/AlphaPeptDeep:SpecMseNorm
ionb_mse_norm	MS2PIP/AlphaPeptDeep:IonBMseNorm
iony_mse_norm	MS2PIP/AlphaPeptDeep:IonYMseNorm
min_abs_diff_norm	MS2PIP/AlphaPeptDeep:MinAbsDiffNorm
max_abs_diff_norm	MS2PIP/AlphaPeptDeep:MaxAbsDiffNorm
abs_diff_Q1_norm	MS2PIP/AlphaPeptDeep:AbsDiffQ1Norm
abs_diff_Q2_norm	MS2PIP/AlphaPeptDeep:AbsDiffQ2Norm
abs_diff_Q3_norm	MS2PIP/AlphaPeptDeep:AbsDiffQ3Norm
mean_abs_diff_norm	MS2PIP/AlphaPeptDeep:MeanAbsDiffNorm
std_abs_diff_norm	MS2PIP/AlphaPeptDeep:StdAbsDiffNorm
ionb_min_abs_diff_norm	MS2PIP/AlphaPeptDeep:IonBMinAbsDiffNorm
ionb_max_abs_diff_norm	MS2PIP/AlphaPeptDeep:IonBMaxAbsDiffNorm
ionb_abs_diff_Q1_norm	MS2PIP/AlphaPeptDeep:IonBAbsDiffQ1Norm
ionb_abs_diff_Q2_norm	MS2PIP/AlphaPeptDeep:IonBAbsDiffQ2Norm
ionb_abs_diff_Q3_norm	MS2PIP/AlphaPeptDeep:IonBAbsDiffQ3Norm
ionb_mean_abs_diff_norm	MS2PIP/AlphaPeptDeep:IonBMeanAbsDiffNorm
ionb_std_abs_diff_norm	MS2PIP/AlphaPeptDeep:IonBStdAbsDiffNorm
iony_min_abs_diff_norm	MS2PIP/AlphaPeptDeep:IonYMinAbsDiffNorm
iony_max_abs_diff_norm	MS2PIP/AlphaPeptDeep:IonYMaxAbsDiffNorm
iony_abs_diff_Q1_norm	MS2PIP/AlphaPeptDeep:IonYAbsDiffQ1Norm
iony_abs_diff_Q2_norm	MS2PIP/AlphaPeptDeep:IonYAbsDiffQ2Norm
iony_abs_diff_Q3_norm	MS2PIP/AlphaPeptDeep:IonYAbsDiffQ3Norm
iony_mean_abs_diff_norm	MS2PIP/AlphaPeptDeep:IonYMeanAbsDiffNorm
iony_std_abs_diff_norm	MS2PIP/AlphaPeptDeep:IonYStdAbsDiffNorm
dotprod_norm	MS2PIP/AlphaPeptDeep:DotProdNorm
dotprod_ionb_norm	MS2PIP/AlphaPeptDeep:DotProdIonBNorm
dotprod_iony_norm	MS2PIP/AlphaPeptDeep:DotProdIonYNorm
cos_ionb_norm	MS2PIP/AlphaPeptDeep:CosIonBNorm
cos_iony_norm	MS2PIP/AlphaPeptDeep:CosIonYNorm
ionb_pearson	MS2PIP/AlphaPeptDeep:IonBPearson
iony_pearson	MS2PIP/AlphaPeptDeep:IonYPearson
spec_spearman	MS2PIP/AlphaPeptDeep:SpecSpearman
ionb_spearman	MS2PIP/AlphaPeptDeep:IonBSpearman
iony_spearman	MS2PIP/AlphaPeptDeep:IonYSpearman
spec_mse	MS2PIP/AlphaPeptDeep:SpecMse
ionb_mse	MS2PIP/AlphaPeptDeep:IonBMse
iony_mse	MS2PIP/AlphaPeptDeep:IonYMse
min_abs_diff_iontype	MS2PIP/AlphaPeptDeep:MinAbsDiffIonType
max_abs_diff_iontype	MS2PIP/AlphaPeptDeep:MaxAbsDiffIonType
min_abs_diff	MS2PIP/AlphaPeptDeep:MinAbsDiff
max_abs_diff	MS2PIP/AlphaPeptDeep:MaxAbsDiff
abs_diff_Q1	MS2PIP/AlphaPeptDeep:AbsDiffQ1
abs_diff_Q2	MS2PIP/AlphaPeptDeep:AbsDiffQ2
abs_diff_Q3	MS2PIP/AlphaPeptDeep:AbsDiffQ3
mean_abs_diff	MS2PIP/AlphaPeptDeep:MeanAbsDiff
std_abs_diff	MS2PIP/AlphaPeptDeep:StdAbsDiff
ionb_min_abs_diff	MS2PIP/AlphaPeptDeep:IonBMinAbsDiff
ionb_max_abs_diff	MS2PIP/AlphaPeptDeep:IonBMaxAbsDiff
ionb_abs_diff_Q1	MS2PIP/AlphaPeptDeep:IonBAbsDiffQ1
ionb_abs_diff_Q2	MS2PIP/AlphaPeptDeep:IonBAbsDiffQ2
ionb_abs_diff_Q3	MS2PIP/AlphaPeptDeep:IonBAbsDiffQ3
ionb_mean_abs_diff	MS2PIP/AlphaPeptDeep:IonBMeanAbsDiff
ionb_std_abs_diff	MS2PIP/AlphaPeptDeep:IonBStdAbsDiff
iony_min_abs_diff	MS2PIP/AlphaPeptDeep:IonYMinAbsDiff
iony_max_abs_diff	MS2PIP/AlphaPeptDeep:IonYMaxAbsDiff
iony_abs_diff_Q1	MS2PIP/AlphaPeptDeep:IonYAbsDiffQ1
iony_abs_diff_Q2	MS2PIP/AlphaPeptDeep:IonYAbsDiffQ2
iony_abs_diff_Q3	MS2PIP/AlphaPeptDeep:IonYAbsDiffQ3
iony_mean_abs_diff	MS2PIP/AlphaPeptDeep:IonYMeanAbsDiff
iony_std_abs_diff	MS2PIP/AlphaPeptDeep:IonYStdAbsDiff
dotprod_ionb	MS2PIP/AlphaPeptDeep:DotProdIonB
dotprod_iony	MS2PIP/AlphaPeptDeep:DotProdIonY
cos_ionb	MS2PIP/AlphaPeptDeep:CosIonB
cos_iony	MS2PIP/AlphaPeptDeep:CosIonY

DeepLC Feature Mapping Table

MMS2Rescore DeepLC Feature	quantms-rescoring Name
observed_retention_time	DeepLC:ObservedRetentionTime
predicted_retention_time	DeepLC:PredictedRetentionTime
rt_diff	DeepLC:RtDiff
observed_retention_time_best	DeepLC:ObservedRetentionTimeBest
predicted_retention_time_best	DeepLC:PredictedRetentionTimeBest
rt_diff_best	DeepLC:RtDiffBest

Spectrum Feature Mapping Table

Spectrum Feature	quantms-rescoring Name
snr	Quantms:Snr
spectral_entropy	Quantms:SpectralEntropy
fraction_tic_top_10	Quantms:FracTICinTop10Peaks
weighted_std_mz	Quantms:WeightedStdMz

Data Processing of idXML Files

• Parallel Processing: Implements multiprocessing capabilities for handling large datasets efficiently
• OpenMS Compatibility Layer: Custom helper classes that gather statistics of number of PSMs by MS levels / dissociation methods, etc.
• Feature Validation: Convert all Features from MS2PIP, DeepLC, and quantms into OpenMS features with well-established names (constants.py)
• PSM Filtering and Validation:
  • Filter PSMs with missing spectra information or empty peaks.
  • Breaks the analysis of the input file contains more than one MS level or dissociation method, only support for MS2 level spectra.
• Output / Input files:
  • Only works for OpenMS formats idXML, and mzML as input and export to idXML with the annotated features.

Installation

Install quantms-rescoring using one of the following methods:

Using pip

❯ pip install quantms-rescoring

Using conda

❯ conda install -c bioconda quantms-rescoring

Using Docker

# Pull the latest image from GitHub Container Registry
❯ docker pull ghcr.io/bigbio/quantms-rescoring:latest

# Run the container
❯ docker run --rm ghcr.io/bigbio/quantms-rescoring:latest --help

# Run with data mounted
❯ docker run --rm -v /path/to/data:/data ghcr.io/bigbio/quantms-rescoring:latest rescoring msrescore2feature --help

Build from source:

1. Clone the quantms-rescoring repository:

   ❯ git clone https://github.com/bigbio/quantms-rescoring
   

2. Navigate to the project directory:

   ❯ cd quantms-rescoring
   

3. Install the project dependencies:

   • Using pip:

     ❯ pip install -r requirements.txt
     

   • Using conda:

     ❯ conda env create -f environment.yml
     

4. Install the package using poetry:

   ❯ poetry install
   

Build Docker image locally:

# Build the Docker image
❯ docker build -t quantms-rescoring:latest .

# Test the image
❯ ./scripts/test-docker.sh latest

Offline Model Download

For environments without internet access (e.g., HPC clusters), you can download all required models ahead of time using the download_models command:

# Download all models to default cache locations
❯ rescoring download_models

# Download models to a specific directory
❯ rescoring download_models --model_dir /path/to/models

# Download only specific models
❯ rescoring download_models --models deeplc,alphapeptdeep

# Get help
❯ rescoring download_models --help

This command downloads models for:

• MS2PIP: Fragment ion intensity prediction models (bundled with ms2pip package)
• AlphaPeptDeep: MS2 spectrum, retention time, and CCS prediction models

Once downloaded, you can transfer the models to your offline environment and use them with the processing commands. For AlphaPeptDeep models, use the --ms2_model_dir option when running msrescore2feature.

TODO

• Add support for multiple Files combined idXML and mzML

Issues and Contributions

For any issues or contributions, please open an issue in the GitHub repository - we use the quantms repo to control all issues—or PR in the GitHub repository.