lipidr: A Software Tool for Data Mining and Analysis of Lipidomics Datasets

Area of Science:

• Biochemistry
• Bioinformatics
• Computational Biology

Background:

• Mass spectrometry (MS)-based lipidomics enables simultaneous measurement of numerous lipid classes.
• Increasing availability of lipidomics datasets necessitates specialized software for data analysis and public data mining.
• Lipidomics data analysis requires integration of unique molecular information like lipid class, chain length, and unsaturation.

Purpose of the Study:

• To develop an open-source R/Bioconductor package named lipidr for lipidomics data mining and analysis.
• To provide a comprehensive workflow for both targeted and untargeted lipidomics.
• To facilitate seamless integration and reanalysis of public lipidomics datasets.

Main Methods:

• Developed lipidr, an R/Bioconductor package.
• Implemented automated inference of lipid class and chain information from lipid names.
• Integrated with the Metabolomics Workbench API for public dataset access.
• Incorporated novel lipid set enrichment analysis for interpreting lipid characteristics.
• Provided interactive visualizations for data inspection and analysis.

Main Results:

• lipidr successfully imports various data formats (numerical matrices, Skyline exports, Metabolomics Workbench files).
• The package automatically infers detailed lipid molecular information.
• Users can search, download, and reanalyze public lipidomics datasets.
• Novel lipid set enrichment analysis aids in interpreting lipid class, chain length, and unsaturation.
• Interactive visualizations enhance data exploration and interpretation.

Conclusions:

• lipidr offers a user-friendly and comprehensive solution for lipidomics data analysis.
• The package facilitates novel insights from complex lipidomics datasets.
• lipidr empowers the lipidomics research community through advanced data mining and interpretation tools.