Comprehensive Lipidomic Automation Workflow using Large Language Models
<|reference_start|>Comprehensive Lipidomic Automation Workflow using Large Language Models: Lipidomics generates large data that makes manual annotation and interpretation challenging. Lipid chemical and structural diversity with structural isomers further complicates annotation. Although, several commercial and open-source software for targeted lipid identification exists, it lacks automated method generation workflows and integration with statistical and bioinformatics tools. We have developed the Comprehensive Lipidomic Automated Workflow (CLAW) platform with integrated workflow for parsing, detailed statistical analysis and lipid annotations based on custom multiple reaction monitoring (MRM) precursor and product ion pair transitions. CLAW contains several modules including identification of carbon-carbon double bond position(s) in unsaturated lipids when combined with ozone electrospray ionization (OzESI)-MRM methodology. To demonstrate the utility of the automated workflow in CLAW, large-scale lipidomics data was collected with traditional and OzESI-MRM profiling on biological and non-biological samples. Specifically, a total of 1497 transitions organized into 10 MRM-based mass spectrometry methods were used to profile lipid droplets isolated from different brain regions of 18-24 month-old Alzheimer's disease mice and age-matched wild-type controls. Additionally, triacyclglycerols (TGs) profiles with carbon-carbon double bond specificity were generated from canola oil samples using OzESI-MRM profiling. We also developed an integrated language user interface with large language models using artificially intelligent (AI) agents that permits users to interact with the CLAW platform using a chatbot terminal to perform statistical and bioinformatic analyses. We envision CLAW pipeline to be used in high-throughput lipid structural identification tasks aiding users to generate automated lipidomics workflows ranging from data acquisition to AI agent-based bioinformatic analysis.<|reference_end|>
@article{beveridge2024comprehensive,
    title={Comprehensive Lipidomic Automation Workflow using Large Language Models},
    author={Connor Beveridge, Sanjay Iyer, Caitlin E. Randolph, Matthew Muhoberac,
  Palak Manchanda, Amy C. Clingenpeel, Shane Tichy, Gaurav Chopra},
    journal={arXiv preprint arXiv:2403.15076},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15076},
    primaryClass={q-bio.QM cs.AI q-bio.BM q-bio.SC}
}