ArticleJournal of proteome research2022
Reducing Peptide Sequence Bias in Quantitative Mass Spectrometry Data with Machine Learning.
Article in Journal of proteome research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Integration of NanoMIPs With Gold-Patterned Plasmonic Surfaces as Synthetic Protein A.Small science · 2026Article
- Advances in the application of artificial intelligence in mass spectrometry-based analysis of traditional Chinese medicine: compound identification and metabolic pathway elucidation.Analytical and bioanalytical chemistry · 2026Review
- Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models.Proteomics · 2025Review
- Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification.Molecular & cellular proteomics : MCP · 2024Review
- Amino acid sequence assignment from single molecule peptide sequencing data using a two-stage classifier.PLoS computational biology · 2023Article
- Toward an Integrated Machine Learning Model of a Proteomics Experiment.Journal of proteome research · 2023Review
- Article
- ProteomicsML: An Online Platform for Community-Curated Data sets and Tutorials for Machine Learning in Proteomics.Journal of proteome research · 2023Article
- DeGlyPHER: Highly sensitive site-specific analysis of N-linked glycans on proteins.Methods in enzymology · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Quantitative mass spectrometry measurements of peptides necessarily incorporate sequence-specific biases that reflect the behavior of the peptide during enzymatic digestion and liquid chromatography and in a mass spectrometer. These sequence-specific effects impair quantification accuracy, yielding peptide quantities that are systematically under- or overestimated. We provide empirical evidence for the existence of such biases, and we use a deep neural network, called Pepper, to automatically identify and reduce these biases. The model generalizes to new proteins and new runs within a related set of tandem mass spectrometry experiments, and the learned coefficients themselves reflect expected physicochemical properties of the corresponding peptide sequences. The resulting adjusted abundance measurements are more correlated with mRNA-based gene expression measurements than the unadjusted measurements. Pepper is suitable for data generated on a variety of mass spectrometry instruments and can be used with labeled or label-free approaches and with data-independent or data-dependent acquisition.
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What OpenQuestion holds
Registered trials
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