FineFDR: Fine-grained Taxonomy-specific False Discovery Rates Control in Metaproteomics

Area of Science:

• Microbiology
• Proteomics
• Bioinformatics

Background:

• Metaproteomics identifies all proteins expressed by a microbiota using tandem mass spectrometry (MS/MS).
• A critical post-analysis step is controlling the false discovery rate (FDR) to ensure accurate protein identifications.
• Existing FDR methods treat all peptides and proteins equally, potentially overlooking varied identification probabilities.

Purpose of the Study:

• To develop a novel framework, FineFDR, for assessing the false discovery rate (FDR) in metaproteomics with improved precision.
• To incorporate taxonomic information into FDR estimation for more granular analysis.
• To enhance the accuracy and quantity of peptide and protein identifications in microbial community proteomics.

Main Methods:

• Developed FineFDR, a framework for fine-grained FDR assessment considering taxonomic information.
• Grouped identified peptide-spectrum matches, peptides, and proteins by taxonomic units.
• Estimated FDR independently for each taxonomic group.

Main Results:

• FineFDR demonstrated higher precision in FDR estimation compared to state-of-the-art methods.
• The framework led to a greater number of peptide and protein identifications.
• Empirical experiments on simulated and real-world datasets validated FineFDR's performance.

Conclusions:

• FineFDR offers a more accurate and sensitive approach to FDR control in metaproteomics.
• Incorporating taxonomic information significantly improves the reliability of microbial community proteomic analyses.
• The FineFDR framework enhances the discovery of microbial proteins and peptides.