A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets

Area of Science:

• Proteomics
• Bioinformatics
• Computational Biology

Background:

• Accurate false discovery rate (FDR) estimation is crucial for identifying proteins in large-scale proteomic datasets.
• Current methods, like the classic target-decoy strategy, face challenges with heterogeneity and scale, leading to over-prediction of false positives.
• Lack of consensus exists on protein FDR methodology, hindering reliable data interpretation.

Purpose of the Study:

• To investigate and compare the classic protein FDR estimation with a novel
• picked
• target-decoy approach.
• To evaluate the performance of the
• picked
• strategy using a large, heterogeneous proteomic dataset.
• To address limitations in current protein FDR estimation for large datasets.

Main Methods:

• Utilized a large dataset of approximately 19,000 LC-MS/MS runs from ProteomicsDB.
• Implemented and assessed the
• picked
• protein FDR approach, treating target and decoy sequences as pairs.
• Combined the
• picked
• strategy with q-value based peptide scoring for normalization.

Main Results:

• The
• picked
• target-decoy strategy, using the best peptide q-value for protein scoring, demonstrated superior performance.
• This approach yielded a stable number of true positive protein identifications across various q-value thresholds.
• The strategy effectively eliminated conceptual issues causing overprediction of false positives in the classic approach.

Conclusions:

• The proposed
• picked
• target-decoy strategy provides a simple, unbiased, and scalable method for protein FDR estimation.
• It consistently increases true positive protein identifications and performs well across datasets of varying sizes.
• This approach is readily implementable in standard proteomics analysis software.