ArticleBioinformatics (Oxford, England)2015
Mass spectrometry-based protein identification with accurate statistical significance assignment.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Taxonomic-Level Protein Quantification in Metaproteomics Using a Biomass-Constrained Expectation-Maximization Approach.Journal of the American Society for Mass Spectrometry · 2026Article
- From Identification to Insight: Making Full Use of the Diagnostic Potential of MS/MS Proteotyping in Clinical Microbiology Using Efficient Bioinformatics.Journal of proteome research · 2025Review
- Biological Function Assignment across Taxonomic Levels in Mass-Spectrometry-Based Metaproteomics via a Modified Expectation Maximization Algorithm.Journal of proteome research · 2025Article
- Identification of Antibiotic Resistance Proteins via MiCId's Augmented Workflow. A Mass Spectrometry-Based Proteomics Approach.Journal of the American Society for Mass Spectrometry · 2022Article
- Robust Accurate Identification and Biomass Estimates of Microorganisms via Tandem Mass Spectrometry.Journal of the American Society for Mass Spectrometry · 2020Article
- Beyond mass spectrometry, the next step in proteomics.Science advances · 2020Review
- RAId: Knowledge-Integrated Proteomics Web Service with Accurate Statistical Significance Assignment.Proteomics · 2019Article
- Bayesian Hierarchical Model for Protein Identifications.Journal of applied statistics · 2019Article
- Rapid Classification and Identification of Multiple Microorganisms with Accurate Statistical Significance via High-Resolution Tandem Mass Spectrometry.Journal of the American Society for Mass Spectrometry · 2018Article
- A Protein Standard That Emulates Homology for the Characterization of Protein Inference Algorithms.Journal of proteome research · 2018Article
- A graphical user interface for RAId, a knowledge integrated proteomics analysis suite with accurate statistics.BMC research notes · 2018Article
- Fast and Accurate Protein False Discovery Rates on Large-Scale Proteomics Data Sets with Percolator 3.0.Journal of the American Society for Mass Spectrometry · 2016Article
- Confidence assignment for mass spectrometry based peptide identifications via the extreme value distribution.Bioinformatics (Oxford, England) · 2016Article
- Identification of Microorganisms by High Resolution Tandem Mass Spectrometry with Accurate Statistical Significance.Journal of the American Society for Mass Spectrometry · 2016Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
motivationAssigning statistical significance accurately has become increasingly important as metadata of many types, often assembled in hierarchies, are constructed and combined for further biological analyses. Statistical inaccuracy of metadata at any level may propagate to downstream analyses, undermining the validity of scientific conclusions thus drawn. From the perspective of mass spectrometry-based proteomics, even though accurate statistics for peptide identification can now be achieved, accurate protein level statistics remain challenging.
resultsWe have constructed a protein ID method that combines peptide evidences of a candidate protein based on a rigorous formula derived earlier; in this formula the database P-value of every peptide is weighted, prior to the final combination, according to the number of proteins it maps to. We have also shown that this protein ID method provides accurate protein level E-value, eliminating the need of using empirical post-processing methods for type-I error control. Using a known protein mixture, we find that this protein ID method, when combined with the Sorić formula, yields accurate values for the proportion of false discoveries. In terms of retrieval efficacy, the results from our method are comparable with other methods tested. AVAILABILITY AND IMPLEMENTATION: The source code, implemented in C++ on a linux system, is available for download at ftp://ftp.ncbi.nlm.nih.gov/pub/qmbp/qmbp_ms/RAId/RAId_Linux_64Bit.
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