ArticleMolecular & cellular proteomics : MCP2019
Integrated Identification and Quantification Error Probabilities for Shotgun Proteomics.
Article in Molecular & cellular proteomics : MCP, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrating Multiple Quantitative Proteomic Analyses Using MetaMSD.Methods in molecular biology (Clifton, N.J.) · 2023Pooled it
- Differential protein expression analysis of quantitative mass spectrometry data using DEqMS.Nature protocols · 2026Review
- A Bayesian framework for multivariate differential analysis.PLoS computational biology · 2026Article
- Decoding disease and therapy through multiomics integration and systems analysis.Briefings in bioinformatics · 2026Review
- The Peptonizer2000: Bringing Confidence to Metaproteomics.Journal of proteome research · 2026Article
- A Proteomic Signature for Human Papillomavirus-Associated Oropharyngeal Squamous Cell Carcinoma Predicts Patients at High Risk of Recurrence.Cancer research communications · 2025Article
- CurveCurator: a recalibrated F-statistic to assess, classify, and explore significance of dose-response curves.Nature communications · 2023Article
- Triqler for Protein Summarization of Data from Data-Independent Acquisition Mass Spectrometry.Journal of proteome research · 2023Article
- Toward an Integrated Machine Learning Model of a Proteomics Experiment.Journal of proteome research · 2023Review
- Developing a bioinformatics pipeline for comparative protein classification analysis.BMC genomic data · 2022Article
- Putting Humpty Dumpty Back Together Again: What Does Protein Quantification Mean in Bottom-Up Proteomics?Journal of proteome research · 2022Article
- Challenges and Opportunities for Bayesian Statistics in Proteomics.Journal of proteome research · 2022Review
- Multiple Imputation Approaches Applied to the Missing Value Problem in Bottom-Up Proteomics.International journal of molecular sciences · 2021Article
- Triqler for MaxQuant: Enhancing Results from MaxQuant by Bayesian Error Propagation and Integration.Journal of proteome research · 2021Article
- mokapot: Fast and Flexible Semisupervised Learning for Peptide Detection.Journal of proteome research · 2021Article
- Focus on the spectra that matter by clustering of quantification data in shotgun proteomics.Nature communications · 2020Article
- A Bayesian Null Interval Hypothesis Test Controls False Discovery Rates and Improves Sensitivity in Label-Free Quantitative Proteomics.Journal of proteome research · 2020Article
- Bayesian Confidence Intervals for Multiplexed Proteomics Integrate Ion-statistics with Peptide Quantification Concordance.Molecular & cellular proteomics : MCP · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein quantification by label-free shotgun proteomics experiments is plagued by a multitude of error sources. Typical pipelines for identifying differential proteins use intermediate filters 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,
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Registered trials
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