IsobaricQuant enables cross‐platform quantification, visualization, and filtering of isobarically‐labeled peptides. Issue 19 (11th July 2022)

• Record Type: Journal Article
• Authors: Hogrebe, Alexander; Hess, Kyle N.; Llovet, Ariadna; Ramos, Y. Julian; Barente, Anthony S.; Hernandez‐Portugues, Daniel; Smith, Ian R.; Rodríguez‐Mias, Ricard A.; Villén, Judit
• Editors: Paulo, João A.
• Is Part Of: Proteomics. Volume 22:Issue 19/20(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-07-11
• Subjects: isobaric mass tag -- protein quantification -- quantification software; Proteins -- Separation -- Periodicals; Bioinformatics -- Periodicals; Proteomics -- Periodicals; Genomes -- Periodicals; Molecular genetics -- Periodicals; 572.605
• DOI: 10.1002/pmic.202100253
• Languages: English
• ISSNs: 1615-9853

Abstract

Abstract: In mass spectrometry (MS)‐based quantitative proteomics, labeling with isobaric mass tags such as iTRAQ and TMT can substantially improve sample throughput and reduce peptide missing values. Nonetheless, the quantification of labeled peptides tends to suffer from reduced accuracy due to the co‐isolation of co‐eluting precursors of similar mass‐to‐charge. Acquisition approaches such as multistage MS3 or ion mobility separation address this problem, yet are difficult to audit and limited to expensive instrumentation. Here we introduce IsobaricQuant, an open‐source software tool for quantification, visualization, and filtering of peptides labeled with isobaric mass tags, with specific focus on precursor interference. IsobaricQuant is compatible with MS2 and MS3 acquisition strategies, has a viewer that allows assessing interference, and provides several scores to aid the filtering of scans with compression. We demonstrate that IsobaricQuant quantifications are accurate by comparing it with commonly used software. We further show that its QC scores can successfully filter out scans with reduced quantitative accuracy at MS2 and MS3 levels, removing inaccurate peptide quantifications and decreasing protein CVs. Finally, we apply IsobaricQuant to a PISA dataset and show that QC scores improve the sensitivity of the identification of protein targets of a kinase inhibitor. IsobaricQuant is available at https://github.com/Villen‐Lab/isobaricquant .