A statistical framework for protein quantitation in bottom-up MS-based proteomics
Open accessAbstract
MOTIVATION: Quantitative mass spectrometry-based proteomics requires protein-level estimates and associated confidence measures. Challenges include the presence of low quality or incorrectly identified peptides and informative missingness. Furthermore, models are required for rolling peptide-level information up to the protein level. RESULTS: We present a statistical model that carefully accounts for informative missingness in peak intensities and allows unbiased, model-based, protein-level estimation and inference. The model is applicable to both label-based and label-free quantitation experiments. We also provide automated, model-based, algorithms for filtering of proteins and peptides as well as imputation of missing values. Two LC/MS datasets are used to illustrate the methods. In simulation studies, our methods are shown to achieve substantially more discoveries than standard alternatives. AVAILABILITY: The software has been made available in the open-source proteomics platform DAnTE (http://omics.pnl.gov/software/).
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Peptides may be missing from one or more samples due to a variety of underlying mechanisms, which can result in non-trivial amounts of potential observations being missing.
“Peptides may be missing from one or more samples due to a variety of underlying mechanisms (Daly et al., 2008; Karpievitch et al., 2009), which can result in non-trivial amounts of potential observations being missing.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Karpievitch et al., 2009
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- Authors
- Karpievitch, Y., Stanley, J., Taverner, T., Huang, J., Adkins, J. N., Ansong, C., Heffron, F., Metz, T. O., Qian, W.-J., Yoon, H., Smith, R. D., & Dabney, A. R.
- Affiliations
- Pacific Northwest National Laboratory (US)
- 1 Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, 2Pacific Northwest National Laboratory, P.O. Box 999, Richland, WA 99352 and 3Molecular Microbiology and Immunology, Oregon Health and Science University, Mail Code L220, Portland, OR 97201, USA
- Corresponding author
- Dabney, Alan R.
- ORCID
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-06-17
- Volume
- 25
- Issue
- 16
- Pages
- 2028-2034
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC2723007
- OpenAlex
- W2125419939
- Cited by
- 176
- References
- 29
- Citations by year
- 2026: 5 · 2025: 2 · 2024: 9 · 2023: 8 · 2022: 12 · 2021: 11 · 2020: 8 · 2019: 10 · 2018: 8 · 2017: 10 · 2016: 10 · 2015: 14 · 2014: 20 · 2013: 12 · 2012: 19
- FWCI
- 5.30
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eCite Digital Repository (University of Tasmania) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- U.S. Department of Health and Human Services
- National Institutes of Health (DK070146)
- National Cancer Institute (R25 CA90301, R25CA090301)
- National Institute of Allergy and Infectious Diseases (Y1 AI 489401, R01AI022933)
- National Institute of Diabetes and Digestive and Kidney Diseases (DK070146, R21DK070146, R33DK070146)
- Pacific Northwest National Laboratory
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- protein quantification, shotgun proteomics, label-free quantification, informative missingness, Dante
- MeSH terms
- Proteins, Mass Spectrometry, Models, Statistical, Proteome, Databases, Protein, Proteomics
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