Comprehensive analyses of multiple biological components including nucleic acids, proteins, metabolites, and lipids (i.e. "multiomics") provide unique insights into complex biological processes. Combining insights from these components through multiomics data integration enhances the depth and nuance of biological understanding available from these measurements. Among methods that integrate data across different technologies (e.g. mass spectrometry, sequencing), those that link components based on biological prior knowledge-pathway analyses-represent the most direct way of translating molecular-level observations into meaningful biological insights. However, significant barriers exist that prevent full utilization of metabolomics and especially lipidomics data in pathway integration. Challenges include the fast turnover and complex interactions of small molecules compared to biological macromolecules, low metabolite annotation rates, isomerism among lipids, and a lack of lipid representation in existing pathway knowledge bases. While these issues stem from a variety of causes, improvements to multiomics pathway integration including better incorporation of lipids into pathway knowledge bases and increased adoption of artificial intelligence approaches can greatly enhance the utility of small molecule -omics data, particularly for lipidomics. Here, we describe the current landscape of multiomics integration tools with an emphasis on support for metabolomics and lipidomics data, we highlight their capabilities and gaps with tangible examples using real multiomics data, and provide our perspective on how these approaches can be improved to better support generation of useful biological insights from complex multiomics data.

SUMMARY: Here we introduce Lipid Mini-On, an open-source tool that performs lipid enrichment analyses and visualizations of lipidomics data. Lipid Mini-On uses a text-mining process to bin individual lipid names into multiple lipid ontology groups based on the classification (e.g. LipidMaps) and other characteristics, such as chain length. Lipid Mini-On provides users with the capability to conduct enrichment analysis of the lipid ontology terms using a Shiny app with options of five statistical approaches. Lipid classes can be added to customize the user's database and remain updated as new lipid classes are discovered. Visualization of results is available for all classification options (e.g. lipid subclass and individual fatty acid chains). Results are also visualized through an editable network of relationships between the individual lipids and their associated lipid ontology terms. The utility of the tool is demonstrated using biological (e.g. human lung endothelial cells) and environmental (e.g. peat soil) samples. AVAILABILITY AND IMPLEMENTATION: Rodin (R package: https://github.com/PNNL-Comp-Mass-Spec/Rodin), Lipid Mini-On Shiny app (https://github.com/PNNL-Comp-Mass-Spec/LipidMiniOn) and Lipid Mini-On online tool (https://omicstools.pnnl.gov/shiny/lipid-mini-on/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: We introduce an open-source software, LIQUID, for semi-automated processing and visualization of LC-MS/MS-based lipidomics data. LIQUID provides users with the capability to process high throughput data and contains a customizable target library and scoring model per project needs. The graphical user interface provides visualization of multiple lines of spectral evidence for each lipid identification, allowing rapid examination of data for making confident identifications of lipid molecular species. LIQUID was compared to other freely available software commonly used to identify lipids and other small molecules (e.g. CFM-ID, MetFrag, GNPS, LipidBlast and MS-DIAL), and was found to have a faster processing time to arrive at a higher number of validated lipid identifications. AVAILABILITY AND IMPLEMENTATION: LIQUID is available at http://github.com/PNNL-Comp-Mass-Spec/LIQUID . CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.