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PEPerMINT: peptide abundance imputation in mass spectrometry-based proteomics using graph neural networks
Tobias Pietz1, Sukrit Gupta1,2, Christoph N Schlaffner1,3
1Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, 14482, Germany.
Bioinformatics (Oxford, England)
|September 4, 2024
Summary
PEPerMINT, a novel graph neural network, effectively imputes missing peptide abundance data in mass spectrometry. This method improves quantitative proteomics by addressing missing values and providing uncertainty estimates.
Area of Science:
- Proteomics
- Bioinformatics
- Computational Biology
Background:
- Quantitative proteomics relies on mass spectrometry (MS) for estimating protein abundance.
- Label-free, bottom-up MS protocols digest proteins into peptides for quantification.
- Missing peptide abundance values, often exceeding 50%, hinder accurate protein abundance analysis.
Purpose of the Study:
- To develop a robust method for imputing missing peptide abundance values in quantitative proteomics.
- To improve the accuracy and reliability of downstream proteomic data analyses.
Main Methods:
- Proposed PEPerMINT, a graph neural network model operating at the peptide level.
- Incorporated peptide-to-protein relationships and amino acid sequence information.
- Benchmarked against 11 imputation methods across 6 diverse datasets (cell lines, tissue, plasma).
Main Results:
- PEPerMINT consistently outperformed 11 common imputation methods.
- Maintained high prediction performance across varying missingness levels and evaluation strategies.
- Demonstrated effectiveness in differential expression prediction.
Conclusions:
- PEPerMINT offers a superior solution for imputing missing peptide abundance data.
- Provides valuable uncertainty estimates, allowing users to tailor imputation based on reliability.
- Enhances the accuracy and reliability of quantitative proteomic analyses.
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