Mass spectrometry-based protein identification with accurate statistical significance assignment

Area of Science:

• Proteomics
• Mass Spectrometry
• Bioinformatics

Background:

• Accurate statistical significance assignment is vital for biological analyses using complex metadata.
• Statistical inaccuracies in metadata can compromise downstream scientific conclusions.
• Achieving accurate protein-level statistics remains a challenge in mass spectrometry-based proteomics.

Purpose of the Study:

• To develop a robust protein identification (ID) method for mass spectrometry.
• To improve the accuracy of protein-level statistical significance determination.
• To eliminate the need for empirical post-processing in type-I error control.

Main Methods:

• A novel protein ID method combining peptide evidences using a rigorous formula.
• Weighting peptide database P-values based on the number of proteins they map to.
• Integration with the Sorić formula for false discovery proportion assessment.

Main Results:

• The developed method provides accurate protein-level E-values.
• Eliminates the requirement for empirical post-processing methods for type-I error control.
• Demonstrates accurate false discovery proportion values using a known protein mixture.
• Achieves retrieval efficacy comparable to existing methods.

Conclusions:

• The new protein ID method enhances statistical accuracy in proteomics.
• It offers a reliable approach for protein-level statistical assessment.
• The method is freely available as open-source C++ code.