ArticleJournal of proteome research2019
Evaluating False Transfer Rates from the Match-between-Runs Algorithm with a Two-Proteome Model.
Article in Journal of proteome research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
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Who cites it
59 citing papers in PubMed.
- Article
- Area under the curve quantification outperforms spectral counting in metaproteomics, but matching between runs is detrimental.bioRxiv : the preprint server for biology · 2026Article
- Comprehensive evaluation of statistical approaches for differential metaproteomics.bioRxiv : the preprint server for biology · 2026Article
- Data-independent acquisition-based quantitative proteomics in plants.Frontiers in plant science · 2026Review
- Benchmarking informatics workflows for data-independent acquisition single-cell proteomics.Nature communications · 2025Article
- Scoring information integration with statistical quality control enhanced cross-run analysis of data-independent acquisition proteomics data.Communications chemistry · 2025Article
- Designing a Comparative Proteomics Experiment: Retention-Time Alignment and Imputation Algorithms Affect Statistical Comparisons between Samples.Journal of proteome research · 2025Article
- SLB-msSIM: A Spectral Library-Based Multiplex Segmented SIM Platform for Single-Cell Proteomic Analysis.Proteomics · 2025Article
- Improved Detection of Differentially Abundant Proteins through FDR-Control of Peptide-Identity-Propagation.Journal of proteome research · 2025Article
- On the question of correct use of replicates in quantitative label-free proteomics.Analytical and bioanalytical chemistry · 2025Article
- Skeletal muscle proteomics: considerations and opportunities.npj metabolic health and disease · 2025Review
- FACS-Proteomics strategy toward extracellular vesicles single-phenotype characterization in biological fluids: exploring the role of leukocyte-derived EVs in multiple sclerosis.Journal of translational medicine · 2025Article
- SWAPS: A Modular Deep-Learning Empowered Peptide Identity Propagation Framework Beyond Match-Between-Run.Journal of proteome research · 2025Article
- Methodological Aspects of μLC-MS/MS for Wide-Scale Proteomic Analysis of Anthracycline-Induced Cardiomyopathy.ACS omega · 2025Article
- Isobaric Tagging and Data Independent Acquisition as Complementary Strategies for Proteome Profiling on an Orbitrap Astral Mass Spectrometer.Journal of proteome research · 2025Article
- Enhanced feature matching in single-cell proteomics characterizes IFN-γ response and co-existence of cell states.Nature communications · 2024Article
- The impact of cooking and burial on proteins: a characterisation of experimental foodcrusts and ceramics.Royal Society open science · 2024Article
- Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry.ACS measurement science au · 2024Review
- Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications.Nature communications · 2024Article
- SEMQuant: Extending Sipros-Ensemble with Match-Between-Runs for Comprehensive Quantitative Metaproteomics.Bioinformatics research and applications : ... international symposium, ISBRA ... proceedings. ISBRA (Conference) · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
Stochasticity between independent LC-MS/MS runs is a challenging problem in the field of proteomics, resulting in significant missing values (i.e., abundance measurements) among observed peptides. To address this issue, several approaches have been developed including computational methods such as MaxQuant's match-between-runs (MBR) algorithm. Often dozens of runs are all considered at once by MBR, transferring identifications from any one run to any of the others. To evaluate the error associated with these transfer events, we created a two-sample/two-proteome approach. In this way, samples containing no yeast lysate (
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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.