Cross-Run Hybrid Features Improve the Identification of Data-Independent Acquisition Proteomics

Area of Science:

• Proteomics
• Mass Spectrometry
• Bioinformatics

Background:

• Data-independent acquisition (DIA) mass spectrometry is vital for comprehensive proteomics.
• Traditional single-run DIA analysis methods often lack sufficient identification depth and consistency.

Purpose of the Study:

• To introduce HFDiscrim, a novel multirun DIA analysis tool.
• To enhance the depth and consistency of reliable peptide identifications in DIA proteomics.

Main Methods:

• HFDiscrim was developed as a specialized multirun DIA analysis tool.
• Benchmarking was performed on multiple datasets, including MCB, ccRCC, and a three-species mixture.
• Performance was compared against PyProphet.

Main Results:

• HFDiscrim identified 22.04% more precursors, 19.1% more peptides, and 13.2% more proteins than PyProphet.
• The tool maintained a controllable false discovery rate.
• HFDiscrim demonstrated superior identification rates and improved reproducibility across multiple runs.

Conclusions:

• HFDiscrim significantly advances DIA-MS data analysis by increasing identification depth and consistency.
• The tool provides a more robust approach for reliable peptide and protein identification in proteomics.
• HFDiscrim is publicly available, facilitating its adoption in the research community.