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Integrated Identification and Quantification Error Probabilities for Shotgun Proteomics
- Source :
- Molecular & Cellular Proteomics. 18:561-570
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
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/.
- Subjects :
- Proteomics
False discovery rate
Computer science
Posterior probability
Quantitative proteomics
Word error rate
Biochemistry
Analytical Chemistry
03 medical and health sciences
Tandem Mass Spectrometry
Humans
Graphical model
Databases, Protein
Shotgun proteomics
Molecular Biology
030304 developmental biology
0303 health sciences
business.industry
030302 biochemistry & molecular biology
Probabilistic logic
Technological Innovation and Resources
Bayes Theorem
Pattern recognition
Models, Theoretical
Data set
Identification (information)
Label-free quantification
Urinary Bladder Neoplasms
Artificial intelligence
business
Algorithms
Subjects
Details
- ISSN :
- 15359476
- Volume :
- 18
- Database :
- OpenAIRE
- Journal :
- Molecular & Cellular Proteomics
- Accession number :
- edsair.doi.dedup.....dfc8f18fc33f5e6268e6ab5f95194669