ArticleMolecular & cellular proteomics : MCP2014
Statistical approach to protein quantification.
Article in Molecular & cellular proteomics : MCP, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 38 citations in OpenAlex.
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- SEPepQuant enhances the detection of possible isoform regulations in shotgun proteomics.Nature communications · 2023Article
- Peptidomics Methods Applied to the Study of Flower Development.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Multi-Omics Methods Applied to Flower Development.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Reanalysis of ProteomicsDB Using an Accurate, Sensitive, and Scalable False Discovery Rate Estimation Approach for Protein Groups.Molecular & cellular proteomics : MCP · 2022Article
- Median-Based Absolute Quantification of Proteins Using Fully Unlabeled Generic Internal Standard (FUGIS).Journal of proteome research · 2022Article
- Label-free absolute protein quantification with data-independent acquisition.Journal of proteomics · 2019Article
- Integrated Identification and Quantification Error Probabilities for Shotgun Proteomics.Molecular & cellular proteomics : MCP · 2019Article
- Quantifying Homologous Proteins and Proteoforms.Molecular & cellular proteomics : MCP · 2019Article
- Comparative Hippocampal Synaptic Proteomes of Rodents and Primates: Differences in Neuroplasticity-Related Proteins.Frontiers in molecular neuroscience · 2018Article
- Covariation of Peptide Abundances Accurately Reflects Protein Concentration Differences.Molecular & cellular proteomics : MCP · 2017Article
- Proteomic discovery of host kinase signaling in bacterial infections.Proteomics. Clinical applications · 2016Review
- Direct and Absolute Quantification of over 1800 Yeast Proteins via Selected Reaction Monitoring.Molecular & cellular proteomics : MCP · 2016Article
- Cardiovascular proteomics in the era of big data: experimental and computational advances.Clinical proteomics · 2016Review
- aLFQ: an R-package for estimating absolute protein quantities from label-free LC-MS/MS proteomics data.Bioinformatics (Oxford, England) · 2014Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
A major goal in proteomics is the comprehensive and accurate description of a proteome. This task includes not only the identification of proteins in a sample, but also the accurate quantification of their abundance. Although mass spectrometry typically provides information on peptide identity and abundance in a sample, it does not directly measure the concentration of the corresponding proteins. Specifically, most mass-spectrometry-based approaches (e.g. shotgun proteomics or selected reaction monitoring) allow one to quantify peptides using chromatographic peak intensities or spectral counting information. Ultimately, based on these measurements, one wants to infer the concentrations of the corresponding proteins. Inferring properties of the proteins based on experimental peptide evidence is often a complex problem because of the ambiguity of peptide assignments and different chemical properties of the peptides that affect the observed concentrations. We present SCAMPI, a novel generic and statistically sound framework for computing protein abundance scores based on quantified peptides. In contrast to most previous approaches, our model explicitly includes information from shared peptides to improve protein quantitation, especially in eukaryotes with many homologous sequences. The model accounts for uncertainty in the input data, leading to statistical prediction intervals for the protein scores. Furthermore, peptides with extreme abundances can be reassessed and classified as either regular data points or actual outliers. We used the proposed model with several datasets and compared its performance to that of other, previously used approaches for protein quantification in bottom-up mass spectrometry.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.