ArticleBioinformatics (Oxford, England)2022
Improving confidence in lipidomic annotations by incorporating empirical ion mobility regression analysis and chemical class prediction.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A Polysorbate Structural Atlas Curated from Drift Tube Ion Mobility-Mass Spectrometry Measurements.Analytical chemistry · 2026Article
- Rapid Annotation Strategy forJournal of the American Society for Mass Spectrometry · 2025Article
- Application of lipidomics in the study of traditional Chinese medicine.Journal of pharmaceutical analysis · 2025Review
- An Untargeted Lipidomics Workflow Incorporating High-Resolution Demultiplexing (HRdm) Drift Tube Ion Mobility-Mass Spectrometry.Journal of the American Society for Mass Spectrometry · 2024Article
- Challenges in Lipidomics Biomarker Identification: Avoiding the Pitfalls and Improving Reproducibility.Metabolites · 2024Article
- Computational tools and algorithms for ion mobility spectrometry-mass spectrometry.Proteomics · 2024Review
- Deficiency of the lipid flippase ATP10A causes diet-induced dyslipidemia in female mice.Scientific reports · 2024Article
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6 authors.
Funding
Abstract
motivationMass spectrometry-based untargeted lipidomics aims to globally characterize the lipids and lipid-like molecules in biological systems. Ion mobility increases coverage and confidence by offering an additional dimension of separation and a highly reproducible metric for feature annotation, the collision cross-section (CCS).
resultsWe present a data processing workflow to increase confidence in molecular class annotations based on CCS values. This approach uses class-specific regression models built from a standardized CCS repository (the Unified CCS Compendium) in a parallel scheme that combines a new annotation filtering approach with a machine learning class prediction strategy. In a proof-of-concept study using murine brain lipid extracts, 883 lipids were assigned higher confidence identifications using the filtering approach, which reduced the tentative candidate lists by over 50% on average. An additional 192 unannotated compounds were assigned a predicted chemical class. AVAILABILITY AND IMPLEMENTATION: All relevant source code is available at https://github.com/McLeanResearchGroup/CCS-filter. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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