Common Decoy Distributions Simplify False Discovery Rate Estimation in Shotgun Proteomics.

Area of Science:

• Proteomics
• Bioinformatics
• Statistical Analysis

Background:

• False discovery rate (FDR) estimation is critical for validating peptide-spectrum matches (PSMs) in shotgun proteomics.
• Current target-decoy strategies often yield inaccurate, dataset-specific FDR estimates.
• There is a need for more robust and reliable FDR estimation methods in proteomics.

Purpose of the Study:

• To introduce a novel common decoy distribution (CDD) approach for FDR estimation in proteomics.
• To evaluate the stability and accuracy of the CDD method.
• To challenge the necessity of dataset-specific target-decoy searches.

Main Methods:

• Developed the common decoy distribution (CDD) method utilizing a fixed empirical null score distribution.
• Derived the score distribution from a large corpus of peptide tandem mass spectra.
• Implemented CDD within the PeptideProphet framework and benchmarked against decoy-based PeptideProphet.

Main Results:

• The CDD method demonstrated stability against noise and unexpected peptide modifications.
• CDD-based PeptideProphet showed comparable accuracy in FDR estimation and PSM retrieval to decoy-based methods.
• The study validates the efficacy of a Big Data approach for statistical analysis in proteomics.

Conclusions:

• The common decoy distribution (CDD) offers a viable alternative to traditional target-decoy strategies for FDR control.
• Dataset-specific target-decoy searches may not be essential for accurate FDR estimation.
• Big Data methodologies hold significant potential for advancing statistical applications in proteomics.