ArticleNature communications2025
LipidIN: a comprehensive repository for flash platform-independent annotation and reverse lipidomics.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed.
- Nicotinamide Mononucleotide Combined Glucose Counteracts Iron Overload-Induced Mitochondrial Dysfunction in Mice Under Hypobaric Hypoxia.Molecular & cellular proteomics : MCP · 2026Article
- A lipidomics roadmap: from basic research to societal challenges.Nature communications · 2026Review
- Lipid metabolism and lipid signaling in extracellular vesicles ontogeny: from biogenesis to functional execution.Journal of nanobiotechnology · 2025Review
- Analytical Approaches to the Rapid Characterisation of Marine Glycolipids in Bioproduct Discovery.Marine drugs · 2025Review
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Improving annotation accuracy, coverage, speed and depth of lipid profiles remains a significant challenge in traditional lipid annotation. We introduce LipidIN, an advanced framework designed for flash platform-independent annotation. LipidIN features a 168.5-million lipid fragmentation hierarchical library that encompasses all potential chain compositions and carbon-carbon double bond locations. The expeditious querying module achieves speeds exceeding one hundred billion queries per second across all mass spectral libraries. The lipid categories intelligence model is developed using three relative retention time rules, reducing false positive annotations and predicting unannotated lipids with a 5.7% estimated false discovery rate, covering 8923 lipids cross various species. More importantly, LipidIN integrates a Wide-spectrum Modeling Yield network for regenerating lipid fragment fingerprints to further improve accuracy and coverage with a 20% estimated recall boosting. We further demonstrate the utility of LipidIN in multiple tasks for lipid annotation and biomarker discovery in clinical cohorts.
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Registered trials
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