Journal
JOURNAL OF PROTEOMICS
Volume 230, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.jprot.2020.103985
Keywords
Shotgun proteomics; Protein inference; Label-free quantification; Software
Categories
Funding
- CONACyT Fronteras project [2015-2/814]
- [CONACyT-DFG2016/277850]
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Comparing multiple label-free shotgun proteomics datasets requires various data processing steps, including peptide-spectrum matching, protein inference, and quantification. ProtyQuant is a tool that integrates data from multiple experiments, aiding in protein comparison and providing intuitive information on protein probability and quantity. The accumulated peptide probability (app) serves as a reliable measure of 'Protein Presence,' facilitating protein identification and quantification in comparative proteomics.
Comparing multiple label-free shotgun proteomics datasets requires various data processing and formatting steps, including peptide-spectrum matching, protein inference, and quantification. Finally, the compilation of results files into a format that allows for downstream analyses. ProtyQuant performs protein inference and quantification calculations, and combines the results of individual datasets into plain text tables. These are lightweight, human-readable, and easy to import into databases or statistical software. ProtyQuant reads validated pepXML from proteomic workflows such as the Trans-Proteomic Pipeline (TPP), which makes it compatible with many commercial and free search engines. For protein inference and quantification, a modified version of the PIPQ program (He et al. 2016) was integrated. In contrast to simple spectral-counting, PIPQ sums up peptide probabilities. For assigning peptides to proteins, three algorithms are available: Multiple Counting, Equal Division, and Linear Programming. The accumulated peptide probabilities (app) are used for both tasks, protein probability estimation, and quantification. ProtyQuant was tested using a reference dataset for label-free shotgun proteomics, obtained from different concentrations of 48 human UPS proteins spiked into yeast lysate. Compared to ProteinProphet, ProtyQuant detected up to 126 (15%) more proteins in the mixture, applying an equal false positive rate (FPR). Using the app values for label-free quantification showed suitable sensitivity and linearity. Strikingly, the app values represent a realistic measure of 'Protein Presence,' an integral concept of protein probability and quantity. ProtyQuant provides a graphical user interface (GUI) and scripts for console based processing. It is available (GNU GLP v3) for Windows, Linux, and Docker from https://bitbucket.org/ lababi/protyquant/. Significance: Integrating data from multiple shot-gun proteomics experiments overwhelms non-expert researchers. ProtyQuant complements well-established workflows by aiding the comparison of proteins across samples. Importantly, the probability and abundance of proteins are seen from a holistic point of view. The accumulated peptide probability (app) as an integral measure of 'Protein Presence' demonstrated reliable performance for both protein identification and quantification. Using the app as a single measure facilitates the compilation of reports in comparative proteomics.
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