ReviewJournal of proteomics2010
A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics.
Review in Journal of proteomics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 236 papers, 2 of them syntheses 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
236 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Reproducibility Crossroads: Impact of Statistical Choices on Proteomics Functional Enrichment.International journal of molecular sciences · 2025Pooled it
- Soil and leaf litter metaproteomics-a brief guideline from sampling to understanding.FEMS microbiology ecology · 2016Guideline
- An engineered nanopore identifies saccharides, amino acids, peptides and ribonucleotides.Nature biotechnology · 2026Article
- Improved Protein Identification in Shotgun Proteomics with a Group-Level Extension of the LPGF Model.Journal of proteome research · 2026Article
- AlphaDIA enables DIA transfer learning for feature-free proteomics.Nature biotechnology · 2026Article
- A decision-driven framework for the mass spectrometry analysis of previously uncharacterized protein modifications.STAR protocols · 2026Review
- pAnno: a comprehensive, precise, and fast proteogenomic workflow for the discovery of novel coding regions.Genome biology · 2026Article
- A Framework for Database Search with AI Models in Mass Spectrometry-Based Proteomics.Journal of proteome research · 2026Review
- Harnessing Proteogenomics to Advance Precision Oncology: From Melanoma and Hepatocellular Carcinoma Perspective.Current oncology reports · 2026Review
- Area under the curve quantification outperforms spectral counting in metaproteomics, but matching between runs is detrimental.bioRxiv : the preprint server for biology · 2026Article
- Shotgun proteomics for biomarker discovery in microbial pathogens and allergens in food: a review.Frontiers in bioengineering and biotechnology · 2026Review
- The 'non-conventional' peptidome: A new layer in plant regulatory mechanisms.Plant communications · 2025Review
- Mass spectrometry-based proteomics of FFPE tissues: progress, limitations, and clinical translation barriers.Clinical proteomics · 2025Review
- Protein-level batch-effect correction enhances robustness in MS-based proteomics.Nature communications · 2025Article
- Breaking Boundaries in Histone Modification MS-Based Detection: A Tailored Search Strategy for Unrestricted Identification of Novel Epigenetic Marks.Molecular & cellular proteomics : MCP · 2025Article
- To Fly, or Not to Fly, That Is the Question: A Deep Learning Model for Peptide Detectability Prediction in Mass Spectrometry.Journal of proteome research · 2025Article
- Quetzal: Comprehensive Peptide Fragmentation Annotation and Visualization.Journal of proteome research · 2025Article
- PepCentric Enables Fast Repository-Scale Proteogenomics Searches.bioRxiv : the preprint server for biology · 2025Article
- TopDIA: A Software Tool for Top-Down Data-Independent Acquisition Proteomics.Journal of proteome research · 2025Article
- Grape-Pi: graph-based neural networks for enhanced protein identification in proteomics pipelines.Bioinformatics advances · 2025Article
176 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
This manuscript provides a comprehensive review of the peptide and protein identification process using tandem mass spectrometry (MS/MS) data generated in shotgun proteomic experiments. The commonly used methods for assigning peptide sequences to MS/MS spectra are critically discussed and compared, from basic strategies to advanced multi-stage approaches. A particular attention is paid to the problem of false-positive identifications. Existing statistical approaches for assessing the significance of peptide to spectrum matches are surveyed, ranging from single-spectrum approaches such as expectation values to global error rate estimation procedures such as false discovery rates and posterior probabilities. The importance of using auxiliary discriminant information (mass accuracy, peptide separation coordinates, digestion properties, and etc.) is discussed, and advanced computational approaches for joint modeling of multiple sources of information are presented. This review also includes a detailed analysis of the issues affecting the interpretation of data at the protein level, including the amplification of error rates when going from peptide to protein level, and the ambiguities in inferring the identifies of sample proteins in the presence of shared peptides. Commonly used methods for computing protein-level confidence scores are discussed in detail. The review concludes with a discussion of several outstanding computational issues.
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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.