ArticleNature communications2020
Focus on the spectra that matter by clustering of quantification data in shotgun proteomics.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- The evolving landscape of spatial proteomics technologies in the AI age.Fundamental research · 2026Review
- Article
- Improved Detection of Differentially Abundant Proteins through FDR-Control of Peptide-Identity-Propagation.Journal of proteome research · 2025Article
- DeepRTAlign: toward accurate retention time alignment for large cohort mass spectrometry data analysis.Nature communications · 2023Article
- Toward an Integrated Machine Learning Model of a Proteomics Experiment.Journal of proteome research · 2023Review
- Article
- A Comprehensive Evaluation of Consensus Spectrum Generation Methods in Proteomics.Journal of proteome research · 2022Article
- Divergent and self-reactive immune responses in the CNS of COVID-19 patients with neurological symptoms.Cell reports. Medicine · 2021Article
- Triqler for MaxQuant: Enhancing Results from MaxQuant by Bayesian Error Propagation and Integration.Journal of proteome research · 2021Article
- The one-carbon pool controls mitochondrial energy metabolism via complex I and iron-sulfur clusters.Science advances · 2021Article
- IonQuant Enables Accurate and Sensitive Label-Free Quantification With FDR-Controlled Match-Between-Runs.Molecular & cellular proteomics : MCP · 2021Article
- Evaluation of the site-unspecified peptide identification method for proteolytic peptide mapping.RSC advances · 2020Article
- Robust Summarization and Inference in Proteome-wide Label-free Quantification.Molecular & cellular proteomics : MCP · 2020Article
- Speeding Up Percolator.Journal of proteome research · 2019Article
- Spectral Clustering Improves Label-Free Quantification of Low-Abundant Proteins.Journal of proteome research · 2019Article
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Authors and funding
2 authors.
Funding
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Abstract
In shotgun proteomics, the analysis of label-free quantification experiments is typically limited by the identification rate and the noise level in the quantitative data. This generally causes a low sensitivity in differential expression analysis. Here, we propose a quantification-first approach for peptides that reverses the classical identification-first workflow, thereby preventing valuable information from being discarded in the identification stage. Specifically, we introduce a method, Quandenser, that applies unsupervised clustering on both MS1 and MS2 level to summarize all analytes of interest without assigning identities. This reduces search time due to the data reduction. We can now employ open modification and de novo searches to identify analytes of interest that would have gone unnoticed in traditional pipelines. Quandenser+Triqler outperforms the state-of-the-art method MaxQuant+Perseus, consistently reporting more differentially abundant proteins for all tested datasets. Software is available for all major operating systems at https://github.com/statisticalbiotechnology/quandenser, under Apache 2.0 license.
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