Abstract
BackgroundSeveral methods to handle data generated from bottom-up proteomics via liquid chromatography-mass spectrometry, particularly for peptide-centric quantification dealing with post-translational modification (PTM) analysis like reversible cysteine oxidation are evaluated. The paper proposes a pipeline based on the R programming language to analyze PTMs from peptide-centric label-free quantitative proteomics data.ResultsOur methodology includes variance stabilization, normalization, and missing data imputation to account for the large dynamic range of PTM measurements. It also corrects biases from an enrichment protocol and reduces the random and systematic errors associated with label-free quantification. The performance of the methodology is tested by performing proteome-wide differential PTM quantitation using linear models analysis (limma). We objectively compare two imputation methods along with significance testing when using multiple-imputation for missing data.ConclusionIdentifying PTMs in large-scale datasets is a problem with distinct characteristics that require new methods for handling missing data imputation and differential proteome analysis. Linear models in combination with multiple-imputation could significantly outperform a t-test-based decision method.
Highlights
Several methods to handle data generated from bottom-up proteomics via liquid chromatography-mass spectrometry, for peptide-centric quantification dealing with post-translational modification (PTM) analysis like reversible cysteine oxidation are evaluated
Performance evaluation To assess the impact of missing data in the differential analysis, we provide a comparative analysis of multiple imputations using our empirical distribution-based missing data model with Random Forest imputation [20]
We evaluated the performance of the entire data analysis pipeline using combinations of data processing modules to identify the optimal processing pipeline
Summary
Several methods to handle data generated from bottom-up proteomics via liquid chromatography-mass spectrometry, for peptide-centric quantification dealing with post-translational modification (PTM) analysis like reversible cysteine oxidation are evaluated. Enzyme-catalyzed PTMs, such as acetylation, phosphorylation, or ubiquitination, can occur at one or multiple amino acid residues in a nascent protein following translation and folding, or on mature proteins as part of signal transduction pathways or regulatory/control processes [1]. Technological progress has prompted the development of new protocols for the quantitative analysis of PTMs. Most published work has focused on detecting and quantifying well-known, classical PTMs such as phosphorylation, glycosylation, ubiquitination, and acetylation [4,5,6,7,8,9]. Research on new types of PTMs is emerging; the study of redox-mediated PTMs is currently the
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