ReviewBMC bioinformatics2012
Computational approaches to protein inference in shotgun proteomics.
Review in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed.
- In Depth Characterization of the Promoter Proximal Proteome of Single Copy Locus FOXP2.Molecular & cellular proteomics : MCP · 2026Article
- Proteomics of the Dark-Ventral-Patch Sexual Signal in Male Red Deer.Animals : an open access journal from MDPI · 2025Article
- Functional Analysis of MS-Based Proteomics Data: From Protein Groups to Networks.Molecular & cellular proteomics : MCP · 2024Article
- An algorithm for decoy-free false discovery rate estimation in XL-MS/MS proteomics.Bioinformatics (Oxford, England) · 2024Article
- Challenges and Opportunities for Single-cell Computational Proteomics.Molecular & cellular proteomics : MCP · 2023Review
- Proteomic characterisation of perhexiline treatment on THP-1 M1 macrophage differentiation.Frontiers in immunology · 2023Article
- New mixture models for decoy-free false discovery rate estimation in mass spectrometry proteomics.Bioinformatics (Oxford, England) · 2020Article
- Exploring sample preparation and data evaluation strategies for enhanced identification of host cell proteins in drug products of therapeutic antibodies and Fc-fusion proteins.Analytical and bioanalytical chemistry · 2020Article
- Article
- gpGrouper: A Peptide Grouping Algorithm for Gene-Centric Inference and Quantitation of Bottom-Up Proteomics Data.Molecular & cellular proteomics : MCP · 2018Article
- Proteomic evidence of dietary sources in ancient dental calculus.Proceedings. Biological sciences · 2018Article
- Post-translational modifications as key regulators of apicomplexan biology: insights from proteome-wide studies.Molecular microbiology · 2018Review
- DeepPep: Deep proteome inference from peptide profiles.PLoS computational biology · 2017Article
- MUMAL2: Improving sensitivity in shotgun proteomics using cost sensitive artificial neural networks and a threshold selector algorithm.BMC bioinformatics · 2016Article
- Proteoform-Specific Insights into Cellular Proteome Regulation.Molecular & cellular proteomics : MCP · 2016Article
- Article
- Elucidating Proteoform Families from Proteoform Intact-Mass and Lysine-Count Measurements.Journal of proteome research · 2016Article
- Common errors in mass spectrometry-based analysis of post-translational modifications.Proteomics · 2016Review
- Mass spectrometry-based protein identification with accurate statistical significance assignment.Bioinformatics (Oxford, England) · 2015Article
- Reverse phase protein arrays in signaling pathways: a data integration perspective.Drug design, development and therapy · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Shotgun proteomics has recently emerged as a powerful approach to characterizing proteomes in biological samples. Its overall objective is to identify the form and quantity of each protein in a high-throughput manner by coupling liquid chromatography with tandem mass spectrometry. As a consequence of its high throughput nature, shotgun proteomics faces challenges with respect to the analysis and interpretation of experimental data. Among such challenges, the identification of proteins present in a sample has been recognized as an important computational task. This task generally consists of (1) assigning experimental tandem mass spectra to peptides derived from a protein database, and (2) mapping assigned peptides to proteins and quantifying the confidence of identified proteins. Protein identification is fundamentally a statistical inference problem with a number of methods proposed to address its challenges. In this review we categorize current approaches into rule-based, combinatorial optimization and probabilistic inference techniques, and present them using integer programming and Bayesian inference frameworks. We also discuss the main challenges of protein identification and propose potential solutions with the goal of spurring innovative research in this area.
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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.