The importance of peptide detectability for protein identification, quantification, and experiment design in MS/MS proteomics.

ABSTRACT: Peptide detectability is defined as the probability that a peptide is identified in an LC-MS/MS experiment and has been useful in providing solutions to protein inference and label-free quantification. Previously, predictors for peptide detectability trained on standard or complex samples were proposed. Although the models trained on complex samples may benefit from the large training data sets, it is unclear to what extent they are affected by the unequal abundances of identified proteins. To address this challenge and improve detectability prediction, we present a new algorithm for the iterative learning of peptide detectability from complex mixtures. We provide evidence that the new method approximates detectability with useful accuracy and, based on its design, can be used to interpret

SUBMITTER: Li YF 

PROVIDER: S-EPMC3006185 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature