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Assessing current capabilities for incorporating lipidomics in multiomics data integration
Journal article   Open access   Peer reviewed

Assessing current capabilities for incorporating lipidomics in multiomics data integration

Dylan H Ross, Raghav Jain, Hyeyoon Kim, Javier E Flores, Soumaydeep Sarkar, Chaevien S Clendinen, Jennifer E Kyle, Tao Liu and Sara J C Gosline
Briefings in bioinformatics, Vol.27(3), bbag276
05/04/2026
DOI: 10.1093/bib/bbag276
PMCID: PMC13273573
PMID: 42242681
url
https://doi.org/10.1093/bib/bbag276View
Published (Version of record) Open Access

Abstract

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.
 Animals Computational Biology - methods Humans Lipidomics - methods Metabolomics - methods Multiomics

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