Integrating Identification and Quantification Uncertainty for Differential Protein Abundance Analysis with Triqler

Area of Science:

• Proteomics
• Bioinformatics
• Computational Biology

Background:

• Protein quantification in shotgun proteomics is complex, with errors potentially introduced at multiple stages.
• Accurate quantification is crucial for identifying differentially abundant proteins.

Purpose of the Study:

• To introduce Triqler, a Python package for robust protein quantification.
• To develop a Bayesian model that integrates errors from the label-free quantification pipeline.
• To improve the estimation of differential abundance false discovery rate (FDR).

Main Methods:

• Triqler employs a Bayesian approach to model errors in protein quantification.
• It weighs quantitative values by the confidence in peptide-spectrum match (PSM) correctness.
• Missing values are handled to reflect their uncertainty relative to observed values.

Main Results:

• The model integrates errors from quantification and identification steps.
• It provides a unified differential abundance FDR that accounts for various uncertainties.
• The tutorial demonstrates data generation, Triqler execution, result interpretation, and hyperparameter verification.

Conclusions:

• Triqler offers a comprehensive framework for error estimation in label-free protein quantification.
• The package enhances the reliability of differential abundance analysis by incorporating identification uncertainties.
• This approach leads to more accurate FDR estimation in proteomics studies.