Qualitative and Quantitative Shotgun Proteomics Data Analysis from Data-Dependent Acquisition Mass Spectrometry

Shotgun proteomics is the inferential analysis of proteoforms using peptide proxies produced by enzyme-catalyzed hydrolysis of entire proteomes. Such peptides are usually identified by nanoflow liquid chromatography coupled to tandem mass spectrometry analysis (nLC-MS/MS). Traditionally, MS/MS analysis is performed in data-dependent acquisition (DDA) mode, which usually produces a pattern of fragment masses unique to a single peptide's fragmentation. Here, I describe a statistically rigorous qualitative and quantitative computational analysis for shotgun proteomics DDA analysis using free open-source software tools. MS/MS data are used to identify peptides, and the area of peptide mass/charge over chromatographic elution is used to quantify peptides. All peptides that uniquely map to a protein sequence predicted from the genome are combined into a single protein quantity, which can then be compared across experimental conditions. Statistically significant protein changes can be summarized using gene ontology or pathway term enrichment analysis.