ProtyQuant: Comparing label-free shotgun proteomics datasets using accumulated peptide probabilities

Comparing multiple label-free shotgun proteomics datasets requires various data processing and formatting steps, including peptide-spectrum matching, protein inference, quantification. Finally, the compilation of results files into a format that allows for downstream analyses. ProtyQuant performs inference quantification calculations, combines individual plain text tables. These are lightweight, human-readable, easy to import databases or statistical software. reads validated pepXML from proteomic workflows such as Trans-Proteomic Pipeline (TPP), which makes it compatible with many commercial free search engines. For quantification, modified version PIPQ program (He et al. 2016) was integrated. In contrast simple spectral-counting, sums up peptide probabilities. assigning peptides proteins, three algorithms available: Multiple Counting, Equal Division, Linear Programming. The accumulated probabilities (app) used both tasks, probability estimation, tested using reference dataset proteomics, obtained different concentrations 48 human UPS proteins spiked yeast lysate. Compared ProteinProphet, detected 126 (15%) more in mixture, applying an equal false positive rate (FPR). Using app values showed suitable sensitivity linearity. Strikingly, represent realistic measure ‘Protein Presence,’ integral concept quantity. provides graphical user interface (GUI) scripts console-based processing. It is available (GNU GLP v3) Windows, Linux, Docker https://bitbucket.org/lababi/protyquant/. Integrating shot-gun experiments overwhelms non-expert researchers. complements well-established by aiding comparison across samples. Importantly, abundance seen holistic point view. Presence’ demonstrated reliable performance identification single facilitates reports comparative proteomics.