PEPerMINT: peptide abundance imputation in mass spectrometry-based proteomics using graph neural networks.

ABSTRACT:

Motivation

Accurate quantitative information about protein abundance is crucial for understanding a biological system and its dynamics. Protein abundance is commonly estimated using label-free, bottom-up mass spectrometry (MS) protocols. Here, proteins are digested into peptides before quantification via MS. However, missing peptide abundance values, which can make up more than 50% of all abundance values, are a common issue. They result in missing protein abundance values, which then hinder accurate and reliable downstream analyses.

Results

To impute missing abundance values, we propose PEPerMINT, a graph neural network model working directly on the peptide level that flexibly takes both peptide-to-protein relationships in a graph format as well as amino acid sequence infor

SUBMITTER: Pietz T 

PROVIDER: S-EPMC11373339 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature