ArticleJournal of proteomics2013
Determining the calibration of confidence estimation procedures for unique peptides in shotgun proteomics.
Article in Journal of proteomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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
27 citing papers in PubMed.
- Enhancing peptide identification in metaproteomics through curriculum learning in deep learning.Nature communications · 2025Article
- Detection and Quantification of Drug-Protein Adducts in Human Liver.Journal of proteome research · 2024Article
- Data-Independent Acquisition: A Milestone and Prospect in Clinical Mass Spectrometry-Based Proteomics.Molecular & cellular proteomics : MCP · 2024Review
- APIR: Aggregating Universal Proteomics Database Search Algorithms for Peptide Identification with FDR Control.Genomics, proteomics & bioinformatics · 2024Article
- Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform.Nature communications · 2023Article
- Mzion enables deep and precise identification of peptides in data-dependent acquisition proteomics.Scientific reports · 2023Article
- Triqler for Protein Summarization of Data from Data-Independent Acquisition Mass Spectrometry.Journal of proteome research · 2023Article
- Integrating Identification and Quantification Uncertainty for Differential Protein Abundance Analysis with Triqler.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
- False discovery rate estimation using candidate peptides for each spectrum.BMC bioinformatics · 2022Article
- Improving Peptide-Level Mass Spectrometry Analysis via Double Competition.Journal of proteome research · 2022Article
- Physiological and molecular responses of lobe coral indicate nearshore adaptations to anthropogenic stressors.Scientific reports · 2021Article
- Semi-supervised learning for somatic variant calling and peptide identification in personalized cancer immunotherapy.BMC bioinformatics · 2020Article
- Metaproteomics reveal that rapid perturbations in organic matter prioritize functional restructuring over taxonomy in western Arctic Ocean microbiomes.The ISME journal · 2020Article
- A high-speed search engine pLink 2 with systematic evaluation for proteome-scale identification of cross-linked peptides.Nature communications · 2019Article
- Integrated Identification and Quantification Error Probabilities for Shotgun Proteomics.Molecular & cellular proteomics : MCP · 2019Article
- Joint Precursor Elution Profile Inference via Regression for Peptide Detection in Data-Independent Acquisition Mass Spectra.Journal of proteome research · 2019Article
- Comprehensive identification of peptides in tandem mass spectra using an efficient open search engine.Nature biotechnology · 2018Article
- A Protein Standard That Emulates Homology for the Characterization of Protein Inference Algorithms.Journal of proteome research · 2018Article
- PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data.Nature methods · 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
4 authors.
Funding
Abstract
The analysis of a shotgun proteomics experiment results in a list of peptide-spectrum matches (PSMs) in which each fragmentation spectrum has been matched to a peptide in a database. Subsequently, most protein inference algorithms rank peptides according to the best-scoring PSM for each peptide. However, there is disagreement in the scientific literature on the best method to assess the statistical significance of the resulting peptide identifications. Here, we use a previously described calibration protocol to evaluate the accuracy of three different peptide-level statistical confidence estimation procedures: the classical Fisher's method, and two complementary procedures that estimate significance, respectively, before and after selecting the top-scoring PSM for each spectrum. Our experiments show that the latter method, which is employed by MaxQuant and Percolator, produces the most accurate, well-calibrated results.
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.