Integrated Identification and Quantification Error Probabilities for Shotgun Proteomics
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Abstract
Abstract Protein quantification by label-free shotgun proteomics experiments is plagued by a multitude of error sources. Typical pipelines for identifying differentially expressed proteins use intermediate filters in an attempt to control the error rate. However, they often ignore certain error sources and, moreover, regard filtered lists as completely correct in subsequent steps. These two indiscretions can easily lead to a loss of control of the false discovery rate (FDR). We propose a probabilistic graphical model, Triqler , that propagates error information through all steps, employing distributions in favor of point estimates, most notably for missing value imputation. The model outputs posterior probabilities for fold changes between treatment groups, highlighting uncertainty rather than hiding it. We analyzed 3 engineered datasets and achieved FDR control and high sensitivity, even for truly absent proteins. In a bladder cancer clinical dataset we discovered 35 proteins at 5% FDR, whereas the original study discovered 1 and MaxQuant/Perseus 4 proteins at this threshold. Compellingly, these 35 proteins showed enrichment for functional annotation terms, whereas the top ranked proteins reported by MaxQuant/Perseus showed no enrichment. The model executes in minutes and is freely available at https://pypi.org/project/triqler/ .
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Outlier-exclusion methods improve protein concentration estimates but still report only one protein quantity and ignore peptides that do not agree with that value.
“While these all demonstrate improved protein concentration estimates, they still only report a single protein quantity and ignore peptides that do not agree with that single value.”
From Putting humpty dumpty back together again: what does protein quantification mean in bottom-up proteomics? (2022) · cites this paper as [31]
Several proposed methods exclude peptide measurements from the aggregate protein quantity when they are outliers relative to other peptides mapping to the same protein-coding gene.
“The approach of several proposed methods focuses on the exclusion of peptide measurements from inclusion in the aggregate protein quantity if they are outliers from other peptide measurements mapping to the same protein coding gene.(28–33)”
From Putting humpty dumpty back together again: what does protein quantification mean in bottom-up proteomics? (2022) · cites this paper as [31]
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- Authors
- The, M., & Käll, L.
- Affiliations
- Science for Life Laboratory (SE)
- Corresponding author
- Käll, Lukas
- Venue
- Molecular & Cellular Proteomics
- Publisher
- Elsevier BV
- Published
- 2019-03
- Volume
- 18
- Issue
- 3
- Pages
- 561-570
- ISSN
- 1535-9476 (ISSN-L)
- PMC
- PMC6398204
- OpenAlex
- W2949908578
- Cited by
- 37
- References
- 42
- Citations by year
- 2026: 5 · 2025: 2 · 2024: 4 · 2023: 5 · 2022: 8 · 2021: 8 · 2020: 2 · 2019: 3
- FWCI
- 1.40
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Molecular & Cellular Proteomics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Vetenskapsrådet (2017–04030)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
- Keywords
- label-free quantitative proteomics, false discovery rate control, error propagation, probabilistic graphical models, missing data imputation, bladder cancer
- MeSH terms
- Algorithms, Bayes Theorem, Urinary Bladder Neoplasms, Humans, Models, Theoretical, Databases, Protein, Proteomics, Tandem Mass Spectrometry
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