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.

MOTIVATION: Modern transcriptomics and proteomics enable us to survey the expression of RNAs and proteins at large scales. While these data are usually generated and analyzed separately, there is an increasing interest in comparing and co-analyzing transcriptome and proteome expression data. A major open question is whether transcriptome and proteome expression is linked and how it is coordinated. RESULTS: Here we have developed a probabilistic clustering model that permits analysis of the links between transcriptomic and proteomic profiles in a sensible and flexible manner. Our coupled mixture model defines a prior probability distribution over the component to which a protein profile should be assigned conditioned on which component the associated mRNA profile belongs to. We apply this approach to a large dataset of quantitative transcriptomic and proteomic expression data obtained from a human breast epithelial cell line (HMEC). The results reveal a complex relationship between transcriptome and proteome with most mRNA clusters linked to at least two protein clusters, and vice versa. A more detailed analysis incorporating information on gene function from the Gene Ontology database shows that a high correlation of mRNA and protein expression is limited to the components of some molecular machines, such as the ribosome, cell adhesion complexes and the TCP-1 chaperonin involved in protein folding. AVAILABILITY: Matlab code is available from the authors on request.