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LipidFinder: a computational workflow for discovery of lipids identifies eicosanoid-phosphoinositides in platelets

O'Connor, Anne ORCID:, Brasher, Christopher, Slatter, David, Meckelmann, Sven, Hawksworth, Jade, Allen, Stuart ORCID: and O'Donnell, Valerie ORCID: 2017. LipidFinder: a computational workflow for discovery of lipids identifies eicosanoid-phosphoinositides in platelets. JCI Insight 2 (7) , e91634. 10.1172/jci.insight.91634

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Accurate and high-quality curation of lipidomic datasets generated from plasma, cells, or tissues is becoming essential for cell biology investigations and biomarker discovery for personalized medicine. However, a major challenge lies in removing artifacts otherwise mistakenly interpreted as real lipids from large mass spectrometry files (>60 K features), while retaining genuine ions in the dataset. This requires powerful informatics tools; however, available workflows have not been tailored specifically for lipidomics, particularly discovery research. We designed LipidFinder, an open-source Python workflow. An algorithm is included that optimizes analysis based on users’ own data, and outputs are screened against online databases and categorized into LIPID MAPS classes. LipidFinder outperformed three widely used metabolomics packages using data from human platelets. We show a family of three 12-hydroxyeicosatetraenoic acid phosphoinositides (16:0/, 18:1/, 18:0/12-HETE-PI) generated by thrombin-activated platelets, indicating crosstalk between eicosanoid and phosphoinositide pathways in human cells. The software is available on GitHub (, with full userguides.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Computer Science & Informatics
ISSN: 2379-3708
Date of First Compliant Deposit: 10 August 2017
Date of Acceptance: 14 February 2017
Last Modified: 20 Jan 2024 14:50

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