ArticleMolecular & cellular proteomics : MCP2015
Using Data Independent Acquisition (DIA) to Model High-responding Peptides for Targeted Proteomics Experiments.
Article in Molecular & cellular proteomics : MCP, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 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
36 citing papers in PubMed.
- Software Pipelines for Targeted and Untargeted Proteomics in Bioengineering Applications.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Repurposing the liquid-based Pap test for the detection of ovarian cancer protein biomarkers.Clinical proteomics · 2026Article
- Prioritizing peptides for targeted mass spectrometry experiments using deep learning.bioRxiv : the preprint server for biology · 2026Article
- Development of Highly Multiplex Targeted Proteomics Assays in Biofluids Using a Nominal Mass Ion Trap Mass Spectrometer.Molecular & cellular proteomics : MCP · 2026Article
- The State of Peptide Detectability in Computational Proteomics and Guidelines for AI Applications.Computational and structural biotechnology journal · 2026Article
- Data Independent Acquisition to Inform the Development of Targeted Proteomics Assays Using a Triple Quadrupole Mass Spectrometer.Journal of proteome research · 2025Article
- Rapid assay development for low input targeted proteomics using a versatile linear ion trap.Nature communications · 2025Article
- Absolute quantification of tumor necrosis factor-alpha by isotope dilution mass spectrometry.Frontiers in chemistry · 2025Article
- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- A protein-protein interaction analysis tool for targeted cross-linking mass spectrometry.Scientific reports · 2023Article
- Assessment and Prediction of Human Proteotypic Peptide Stability for Proteomics Quantification.Analytical chemistry · 2023Article
- 2019 Association of Biomolecular Resource Facilities Multi-Laboratory Data-Independent Acquisition Proteomics Study.Journal of biomolecular techniques : JBT · 2023Article
- Mass spectrometry-based phosphoproteomics in clinical applications.Trends in analytical chemistry : TRAC · 2023Article
- A peptide-centric quantitative proteomics dataset for the phenotypic assessment of Alzheimer's disease.Scientific data · 2023Article
- Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS: A deep learning approach.Computational and structural biotechnology journal · 2023Article
- Reducing Peptide Sequence Bias in Quantitative Mass Spectrometry Data with Machine Learning.Journal of proteome research · 2022Article
- A TRUSTED targeted mass spectrometry assay for pan-herpesvirus protein detection.Cell reports · 2022Article
- SWATH-MS and MRM: Quantification of Ras-related proteins in HIV-1 infected and methamphetamine-exposed human monocyte-derived macrophages (hMDM).Proteomics · 2021Article
- CIDer: A Statistical Framework for Interpreting Differences in CID and HCD Fragmentation.Journal of proteome research · 2021Article
- Effects of High Temperature on Rice Grain Development and Quality Formation Based on Proteomics Comparative Analysis Under Field Warming.Frontiers in plant science · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Targeted mass spectrometry is an essential tool for detecting quantitative changes in low abundant proteins throughout the proteome. Although selected reaction monitoring (SRM) is the preferred method for quantifying peptides in complex samples, the process of designing SRM assays is laborious. Peptides have widely varying signal responses dictated by sequence-specific physiochemical properties; one major challenge is in selecting representative peptides to target as a proxy for protein abundance. Here we present PREGO, a software tool that predicts high-responding peptides for SRM experiments. PREGO predicts peptide responses with an artificial neural network trained using 11 minimally redundant, maximally relevant properties. Crucial to its success, PREGO is trained using fragment ion intensities of equimolar synthetic peptides extracted from data independent acquisition experiments. Because of similarities in instrumentation and the nature of data collection, relative peptide responses from data independent acquisition experiments are a suitable substitute for SRM experiments because they both make quantitative measurements from integrated fragment ion chromatograms. Using an SRM experiment containing 12,973 peptides from 724 synthetic proteins, PREGO exhibits a 40-85% improvement over previously published approaches at selecting high-responding peptides. These results also represent a dramatic improvement over the rules-based peptide selection approaches commonly used in the literature.
Indexed as
Identifiers
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.