ReviewMolecular & cellular proteomics : MCP2013
Tools for label-free peptide quantification.
Review in Molecular & cellular proteomics : MCP, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers.
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Who cites it
88 citing papers in PubMed.
- Reliable Identification of Cardiac Maturation Markers Using Robust and Flexible Label-Free Proteomic Quantitation by Spectral Counting on Relatively Abundant Proteins.Journal of proteome research · 2026Article
- Orthogonal validation of PD Biomarkers: Multi-platform proteomics profiling of CSF, Plasma, and Urine confirms DDC as a consistent candidate.medRxiv : the preprint server for health sciences · 2025Article
- Evaluation of protein extraction methodologies on bacterial proteomic profiling: a comparative analysis.Frontiers in microbiology · 2025Article
- Identification of Protein Networks and Biological Pathways Driving the Progression of Atherosclerosis in Human Carotid Arteries Through Mass Spectrometry-Based Proteomics.International journal of molecular sciences · 2024Article
- Review
- LFQ-Based Peptide and Protein Intensity Differential Expression Analysis.Journal of proteome research · 2023Article
- MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale.Journal of proteome research · 2023Article
- Proteomics reveals that cell density could affect the efficacy of drug treatment.Biochemistry and biophysics reports · 2023Article
- Article
- Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice.Proteomes · 2023Review
- Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS: A deep learning approach.Computational and structural biotechnology journal · 2023Article
- A proteome-wide map of chaperone-assisted protein refolding in a cytosol-like milieu.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Article
- Review
- Article
- A knowledge graph to interpret clinical proteomics data.Nature biotechnology · 2022Article
- Bioinformatic Prediction and Characterization of Proteins inFrontiers in nutrition · 2022Article
- Application and Perspectives of MALDI-TOF Mass Spectrometry in Clinical Microbiology Laboratories.Microorganisms · 2021Review
- Analysing omics data sets with weighted nodes networks (WNNets).Scientific reports · 2021Article
- Comparative Evaluation of MaxQuant and Proteome Discoverer MS1-Based Protein Quantification Tools.Journal of proteome research · 2021Article
28 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing scale and complexity of quantitative proteomics studies complicate subsequent analysis of the acquired data. Untargeted label-free quantification, based either on feature intensities or on spectral counting, is a method that scales particularly well with respect to the number of samples. It is thus an excellent alternative to labeling techniques. In order to profit from this scalability, however, data analysis has to cope with large amounts of data, process them automatically, and do a thorough statistical analysis in order to achieve reliable results. We review the state of the art with respect to computational tools for label-free quantification in untargeted proteomics. The two fundamental approaches are feature-based quantification, relying on the summed-up mass spectrometric intensity of peptides, and spectral counting, which relies on the number of MS/MS spectra acquired for a certain protein. We review the current algorithmic approaches underlying some widely used software packages and briefly discuss the statistical strategies for analyzing the data.
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