ArticleProteomics2016
How to talk about protein-level false discovery rates in shotgun proteomics.
Article in Proteomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Using proteomics to advance the search for potential biomarkers for preeclampsia: A systematic review and meta-analysis.PloS one · 2019Pooled it
- Improved Protein Identification in Shotgun Proteomics with a Group-Level Extension of the LPGF Model.Journal of proteome research · 2026Article
- Substrate stiffness-dependent metabolic reprogramming of iPSC-derived cardiomyocytes on physiological PDMS polymers.Metabolic engineering communications · 2025Article
- A Review of Protein Inference.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Reanalysis of ProteomicsDB Using an Accurate, Sensitive, and Scalable False Discovery Rate Estimation Approach for Protein Groups.Molecular & cellular proteomics : MCP · 2022Article
- Improving Peptide-Level Mass Spectrometry Analysis via Double Competition.Journal of proteome research · 2022Article
- Outer Membrane Vesicles Secreted byACS omega · 2022Article
- Proteomes Are of Proteoforms: Embracing the Complexity.Proteomes · 2021Review
- The Choice of Search Engine Affects Sequencing Depth and HLA Class I Allele-Specific Peptide Repertoires.Molecular & cellular proteomics : MCP · 2021Article
- A Global Screen for Assembly State Changes of the Mitotic Proteome by SEC-SWATH-MS.Cell systems · 2020Article
- Proteomics Pipeline for Identifying Variant Proteins inJournal of proteome research · 2019Article
- Progress and Challenges in Ocean Metaproteomics and Proposed Best Practices for Data Sharing.Journal of proteome research · 2019Review
- Integrated Identification and Quantification Error Probabilities for Shotgun Proteomics.Molecular & cellular proteomics : MCP · 2019Article
- Characterization of paucibacillary ileal lesions in sheep with subclinical active infection by Mycobacterium avium subsp. paratuberculosis.Veterinary research · 2018Article
- ABRF Proteome Informatics Research Group (iPRG) 2016 Study: Inferring Proteoforms from Bottom-up Proteomics Data.Journal of biomolecular techniques : JBT · 2018Article
- A Protein Standard That Emulates Homology for the Characterization of Protein Inference Algorithms.Journal of proteome research · 2018Article
- Cost-effective generation of precise label-free quantitative proteomes in high-throughput by microLC and data-independent acquisition.Scientific reports · 2018Article
- Significance estimation for large scale metabolomics annotations by spectral matching.Nature communications · 2017Article
- Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses.Nature methods · 2017Article
- Covariation of Peptide Abundances Accurately Reflects Protein Concentration Differences.Molecular & cellular proteomics : MCP · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A frequently sought output from a shotgun proteomics experiment is a list of proteins that we believe to have been present in the analyzed sample before proteolytic digestion. The standard technique to control for errors in such lists is to enforce a preset threshold for the false discovery rate (FDR). Many consider protein-level FDRs a difficult and vague concept, as the measurement entities, spectra, are manifestations of peptides and not proteins. Here, we argue that this confusion is unnecessary and provide a framework on how to think about protein-level FDRs, starting from its basic principle: the null hypothesis. Specifically, we point out that two competing null hypotheses are used concurrently in today's protein inference methods, which has gone unnoticed by many. Using simulations of a shotgun proteomics experiment, we show how confusing one null hypothesis for the other can lead to serious discrepancies in the FDR. Furthermore, we demonstrate how the same simulations can be used to verify FDR estimates of protein inference methods. In particular, we show that, for a simple protein inference method, decoy models can be used to accurately estimate protein-level FDRs for both competing null hypotheses.
Indexed as
Identifiers
What OpenQuestion holds
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.