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Shotgun Lipidomics of Rodent Tissues
11:46

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Published on: November 18, 2022

Prototype semantic infrastructure for automated small molecule classification and annotation in lipidomics.

Leonid L Chepelev1, Alexandre Riazanov, Alexandre Kouznetsov

  • 1Department of Biology, Carleton University, Ottawa, Canada.

BMC Bioinformatics
|July 28, 2011
PubMed
Summary

Automated lipid classification is now possible using semantic web technologies. This approach accurately annotates and classifies lipids, integrating data for enhanced lipidomics research.

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput experimentation has led to a surge in identified lipid entities, overwhelming manual annotation processes.
  • Accurate, structure-based classification and annotation are crucial for understanding lipid functionality.
  • Novel lipid entities often remain unclassified due to limitations in current annotation methods.

Purpose of the Study:

  • To investigate the utility of semantic web technologies for automated chemical classification and annotation of lipids.
  • To develop a prototype framework for structure-based lipid classification and data integration.
  • To enhance the analysis of high-throughput lipidomics data.

Main Methods:

  • Developed a prototype framework comprising a formal lipid ontology and federated semantic web services.
  • Utilized the Semantic Annotation, Discovery, and Integration (SADI) framework for service deployment.
  • Implemented core services for structural annotation and ontology-based classification, supplemented by services for protein association and publication retrieval.

Main Results:

  • The prototype framework achieved accurate automated classification of lipids.
  • Demonstrated facile integration of lipid class information with additional data via SADI web services.
  • Analyzed SADI-enabled eicosanoid classification against LIPID MAPS, highlighting the integrative methodology's contribution.

Conclusions:

  • Semantic web technologies offer an accurate and versatile approach to lipid classification and annotation.
  • The SADI framework enables programming-free integration of external web services, fostering novel lipidomics applications.
  • The developed prototype demonstrates the potential of automated methods in managing and analyzing large lipid datasets.