Evaluation of linear models and missing value imputation for the analysis of peptide-centric proteomics.

ABSTRACT:

Background

Several 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.

Results

Our 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 p

SUBMITTER: Berg P 

PROVIDER: S-EPMC6419331 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature