Critical evaluation of the use of artificial data for machine learning based de novo peptide identification.

ABSTRACT: Proteins are essential components of all living cells and so the study of their in situ expression, proteomics, has wide reaching applications. Peptide identification in proteomics typically relies on matching high resolution tandem mass spectra to a protein database but can also be performed de novo. While artificial spectra have been successfully incorporated into database search pipelines to increase peptide identification rates, little work has been done to investigate the utility of artificial spectra in the context of de novo peptide identification. Here, we perform a critical analysis of the use of artificial data for the training and evaluation of de novo peptide identification algorithms. First, we classify the different fragment ion types present in real spec

SUBMITTER: McDonnell K 

PROVIDER: S-EPMC10165132 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature