1. Quantitative Proteomics in Yeast: From bSLIM and Proteome Discoverer Outputs to Graphical Assessment of the Significance of Protein Quantification Scores
- Author
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Nicolas Sénécaut, Pierre Poulain, Laurent Lignières, Samuel Terrier, Véronique Legros, Guillaume Chevreux, Gaëlle Lelandais, Jean-Michel Camadro, Institut Jacques Monod (IJM (UMR_7592)), Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Institut de Biologie Intégrative de la Cellule (I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), ANR-18-CE44-0014,SLIM-labeling,Protéomique haute performance par réduction de la complexité isotopique in vivo(2018), and Frédéric Devaux
- Subjects
Proteomics ,Minora ,MESH: Isotope Labeling ,Mass spectrometry ,MESH: Peptides ,MESH: Proteomics ,[SDV]Life Sciences [q-bio] ,KNIME ,Proteome Discoverer ,bSLIM ,[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM] ,MESH: Saccharomyces cerevisiae ,Yeast ,Isotopic labeling ,MESH: Proteome ,Metabolism ,[SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN] ,Quantification ,[SDV.MP.MYC]Life Sciences [q-bio]/Microbiology and Parasitology/Mycology - Abstract
Simple light isotope metabolic labeling (bSLIM) is an innovative method to accurately quantify differences in protein abundance at the proteome level in standard bottom-up experiments. The quantification process requires computation of the ratio of intensity of several isotopologs in the isotopic cluster of every identified peptide. Thus, appropriate bioinformatic workflows are required to extract the signals from the instrument files and calculate the required ratio to infer peptide/protein abundance. In a previous study (Sénécaut et al., J Proteome Res 20:1476–1487, 2021), we developed original open-source workflows based on OpenMS nodes implemented in a KNIME working environment. Here, we extend the use of the bSLIM labeling strategy in quantitative proteomics by presenting an alternative procedure to extract isotopolog intensities and process them by taking advantage of new functionalities integrated into the Minora node of Proteome Discoverer 2.4 software. We also present a graphical strategy to evaluate the statistical robustness of protein quantification scores and calculate the associated false discovery rates (FDR). We validated these approaches in a case study in which we compared the differences between the proteomes of two closely related yeast strains.
- Published
- 2022
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