APIR: a universal FDR-control framework for boosting peptide identification power by aggregating multiple proteomics database search algorithms

Abstract

AbstractAdvances in mass spectrometry (MS) have enabled high-throughput analysis of proteomes in biological systems. The state-of-the-art MS data analysis relies on database search algorithms to quantify proteins by identifying peptide-spectrum matches (PSMs), which convert mass spectra to peptide sequences. Different database search algorithms use distinct search strategies and thus may identify unique PSMs. However, no existing approaches can aggregate all user-specified database search algorithms with guaranteed control on the false discovery rate (FDR) and guaranteed increase in the identified peptides. To fill in this gap, we propose a statistical framework, Aggregation of Peptide Identification Results (APIR), that is universally compatible with all database search algorithms. Notably, under a target FDR threshold, APIR is guaranteed to identify at least as many, if not more, peptides as individual database search algorithms do. Evaluation of APIR on a complex protein standard shows that APIR outpowers individual database search algorithms and guarantees the FDR control. Real data studies show that APIR can identify disease-related proteins and post-translational modifications missed by some individual database search algorithms. Note that the APIR framework is easily extendable to aggregating discoveries made by multiple algorithms in other high-throughput biomedical data analysis, e.g., differential gene expression analysis on RNA sequencing data.