ArticleMolecular & cellular proteomics : MCP2016
DeMix-Q: Quantification-Centered Data Processing Workflow.
Article in Molecular & cellular proteomics : MCP, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed.
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- Scoring information integration with statistical quality control enhanced cross-run analysis of data-independent acquisition proteomics data.Communications chemistry · 2025Article
- Improved Detection of Differentially Abundant Proteins through FDR-Control of Peptide-Identity-Propagation.Journal of proteome research · 2025Article
- SWAPS: A Modular Deep-Learning Empowered Peptide Identity Propagation Framework Beyond Match-Between-Run.Journal of proteome research · 2025Article
- A Proteomic Signature for Human Papillomavirus-Associated Oropharyngeal Squamous Cell Carcinoma Predicts Patients at High Risk of Recurrence.Cancer research communications · 2025Article
- Quality Control in the Mass Spectrometry Proteomics Core: A Practical Primer.Journal of biomolecular techniques : JBT · 2024Article
- Achieving quantitative reproducibility in label-free multisite DIA experiments through multirun alignment.Communications biology · 2023Article
- Strategies for Increasing the Depth and Throughput of Protein Analysis by plexDIA.Journal of proteome research · 2023Review
- Mapping the Melanoma Plasma Proteome (MPP) Using Single-Shot Proteomics Interfaced with the WiMT Database.Cancers · 2021Article
- IceR improves proteome coverage and data completeness in global and single-cell proteomics.Nature communications · 2021Article
- Triqler for MaxQuant: Enhancing Results from MaxQuant by Bayesian Error Propagation and Integration.Journal of proteome research · 2021Article
- Parallel Factor Analysis Enables Quantification and Identification of Highly Convolved Data-Independent-Acquired Protein Spectra.Patterns (New York, N.Y.) · 2020Article
- Urinary Protein Biomarker Panel for the Diagnosis of Antibody-Mediated Rejection in Kidney Transplant Recipients.Kidney international reports · 2020Article
- Focus on the spectra that matter by clustering of quantification data in shotgun proteomics.Nature communications · 2020Article
- Comparative Analysis of Quantitative Mass Spectrometric Methods for Subcellular Proteomics.Journal of proteome research · 2020Article
- MS1 ion current-based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts.Mass spectrometry reviews · 2019Review
- Simultaneous Improvement in the Precision, Accuracy, and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains.Molecular & cellular proteomics : MCP · 2019Article
- DART-ID increases single-cell proteome coverage.PLoS computational biology · 2019Article
- SpotLight Proteomics-A IgG-Enrichment Phenotype Profiling Approach with Clinical Implications.International journal of molecular sciences · 2019Article
- Integrated Identification and Quantification Error Probabilities for Shotgun Proteomics.Molecular & cellular proteomics : MCP · 2019Article
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3 authors.
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Abstract
For historical reasons, most proteomics workflows focus on MS/MS identification but consider quantification as the end point of a comparative study. The stochastic data-dependent MS/MS acquisition (DDA) gives low reproducibility of peptide identifications from one run to another, which inevitably results in problems with missing values when quantifying the same peptide across a series of label-free experiments. However, the signal from the molecular ion is almost always present among the MS(1)spectra. Contrary to what is frequently claimed, missing values do not have to be an intrinsic problem of DDA approaches that perform quantification at the MS(1)level. The challenge is to perform sound peptide identity propagation across multiple high-resolution LC-MS/MS experiments, from runs with MS/MS-based identifications to runs where such information is absent. Here, we present a new analytical workflow DeMix-Q (https://github.com/userbz/DeMix-Q), which performs such propagation that recovers missing values reliably by using a novel scoring scheme for quality control. Compared with traditional workflows for DDA as well as previous DIA studies, DeMix-Q achieves deeper proteome coverage, fewer missing values, and lower quantification variance on a benchmark dataset. This quantification-centered workflow also enables flexible and robust proteome characterization based on covariation of peptide abundances.
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