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 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 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.

CLI Tools

 quantms-rescoring msrescore2feature --help

Annotates PSMs with prediction-based features from MS2PIP and DeepLC

 quantms-rescoring add_sage_feature --help

Incorporates additional features from SAGE into idXML files.

 quantms-rescoring spectrum2feature --help

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

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.
• 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 Feature Mapping Table

MMS2Rescore MS2PIP Feature	quantms-rescoring Name
spec_pearson	MS2PIP:SpecPearson
cos_norm	MS2PIP:SpecCosineNorm
spec_pearson_norm	MS2PIP:SpecPearsonNorm
dotprod	MS2PIP:DotProd
ionb_pearson_norm	MS2PIP:IonBPearsonNorm
iony_pearson_norm	MS2PIP:IonYPearsonNorm
spec_mse_norm	MS2PIP:SpecMseNorm
ionb_mse_norm	MS2PIP:IonBMseNorm
iony_mse_norm	MS2PIP:IonYMseNorm
min_abs_diff_norm	MS2PIP:MinAbsDiffNorm
max_abs_diff_norm	MS2PIP:MaxAbsDiffNorm
abs_diff_Q1_norm	MS2PIP:AbsDiffQ1Norm
abs_diff_Q2_norm	MS2PIP:AbsDiffQ2Norm
abs_diff_Q3_norm	MS2PIP:AbsDiffQ3Norm
mean_abs_diff_norm	MS2PIP:MeanAbsDiffNorm
std_abs_diff_norm	MS2PIP:StdAbsDiffNorm
ionb_min_abs_diff_norm	MS2PIP:IonBMinAbsDiffNorm
ionb_max_abs_diff_norm	MS2PIP:IonBMaxAbsDiffNorm
ionb_abs_diff_Q1_norm	MS2PIP:IonBAbsDiffQ1Norm
ionb_abs_diff_Q2_norm	MS2PIP:IonBAbsDiffQ2Norm
ionb_abs_diff_Q3_norm	MS2PIP:IonBAbsDiffQ3Norm
ionb_mean_abs_diff_norm	MS2PIP:IonBMeanAbsDiffNorm
ionb_std_abs_diff_norm	MS2PIP:IonBStdAbsDiffNorm
iony_min_abs_diff_norm	MS2PIP:IonYMinAbsDiffNorm
iony_max_abs_diff_norm	MS2PIP:IonYMaxAbsDiffNorm
iony_abs_diff_Q1_norm	MS2PIP:IonYAbsDiffQ1Norm
iony_abs_diff_Q2_norm	MS2PIP:IonYAbsDiffQ2Norm
iony_abs_diff_Q3_norm	MS2PIP:IonYAbsDiffQ3Norm
iony_mean_abs_diff_norm	MS2PIP:IonYMeanAbsDiffNorm
iony_std_abs_diff_norm	MS2PIP:IonYStdAbsDiffNorm
dotprod_norm	MS2PIP:DotProdNorm
dotprod_ionb_norm	MS2PIP:DotProdIonBNorm
dotprod_iony_norm	MS2PIP:DotProdIonYNorm
cos_ionb_norm	MS2PIP:CosIonBNorm
cos_iony_norm	MS2PIP:CosIonYNorm
ionb_pearson	MS2PIP:IonBPearson
iony_pearson	MS2PIP:IonYPearson
spec_spearman	MS2PIP:SpecSpearman
ionb_spearman	MS2PIP:IonBSpearman
iony_spearman	MS2PIP:IonYSpearman
spec_mse	MS2PIP:SpecMse
ionb_mse	MS2PIP:IonBMse
iony_mse	MS2PIP:IonYMse
min_abs_diff_iontype	MS2PIP:MinAbsDiffIonType
max_abs_diff_iontype	MS2PIP:MaxAbsDiffIonType
min_abs_diff	MS2PIP:MinAbsDiff
max_abs_diff	MS2PIP:MaxAbsDiff
abs_diff_Q1	MS2PIP:AbsDiffQ1
abs_diff_Q2	MS2PIP:AbsDiffQ2
abs_diff_Q3	MS2PIP:AbsDiffQ3
mean_abs_diff	MS2PIP:MeanAbsDiff
std_abs_diff	MS2PIP:StdAbsDiff
ionb_min_abs_diff	MS2PIP:IonBMinAbsDiff
ionb_max_abs_diff	MS2PIP:IonBMaxAbsDiff
ionb_abs_diff_Q1	MS2PIP:IonBAbsDiffQ1
ionb_abs_diff_Q2	MS2PIP:IonBAbsDiffQ2
ionb_abs_diff_Q3	MS2PIP:IonBAbsDiffQ3
ionb_mean_abs_diff	MS2PIP:IonBMeanAbsDiff
ionb_std_abs_diff	MS2PIP:IonBStdAbsDiff
iony_min_abs_diff	MS2PIP:IonYMinAbsDiff
iony_max_abs_diff	MS2PIP:IonYMaxAbsDiff
iony_abs_diff_Q1	MS2PIP:IonYAbsDiffQ1
iony_abs_diff_Q2	MS2PIP:IonYAbsDiffQ2
iony_abs_diff_Q3	MS2PIP:IonYAbsDiffQ3
iony_mean_abs_diff	MS2PIP:IonYMeanAbsDiff
iony_std_abs_diff	MS2PIP:IonYStdAbsDiff
dotprod_ionb	MS2PIP:DotProdIonB
dotprod_iony	MS2PIP:DotProdIonY
cos_ionb	MS2PIP:CosIonB
cos_iony	MS2PIP: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

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

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.