Integrated Identification and Quantification Error Probabilities for Shotgun Proteomics.

Area of Science:

• Proteomics
• Bioinformatics
• Cancer Research

Background:

• Label-free shotgun proteomics is prone to errors affecting protein quantification.
• Current analysis pipelines often fail to control the false discovery rate (FDR) due to ignored error sources and improper handling of filtered data.
• This leads to a loss of sensitivity and biologically relevant findings.

Purpose of the Study:

• To introduce Triqler, a probabilistic graphical model for robust protein quantification in label-free proteomics.
• To improve FDR control and enhance the sensitivity of differential protein identification by propagating error information throughout the analysis.
• To provide a more accurate and sensitive method for analyzing complex biological datasets, such as clinical cancer samples.

Main Methods:

• Developed a probabilistic graphical model (Triqler) that utilizes distributions instead of point estimates for error propagation.
• Implemented a novel missing value imputation strategy based on probabilistic distributions.
• Applied Triqler to engineered datasets and a bladder cancer clinical dataset to assess FDR control and sensitivity.

Main Results:

• Triqler achieved accurate FDR control and high sensitivity on engineered datasets, even for proteins with missing values.
• In a bladder cancer dataset, Triqler identified 35 proteins at 5% FDR, significantly outperforming existing methods (1-4 proteins).
• The proteins identified by Triqler were enriched for relevant functional annotations, unlike those identified by conventional methods.

Conclusions:

• Triqler offers a significant advancement in label-free proteomics by effectively managing error sources and improving FDR control.
• The model's ability to identify a larger set of biologically relevant proteins has implications for biomarker discovery in diseases like cancer.
• Triqler is a fast, freely available computational tool that enhances the reliability and interpretability of proteomics data.