ON DEMAND WEBINAR
Building Better Biology: Integrating Metabolomics in a Multiomics Framework
As multiomics datasets grow in size and complexity, the challenge isn’t generating data—it’s making it meaningful. This webinar will show how integrating metabolomics into your analysis framework brings essential functional context to genomics, transcriptomics and proteomics. Through real-world case studies, you’ll learn how DIABLO and PathIntegrate uncover biologically coherent, pathway-driven results that transform high-dimensional data into actionable insights.
See how adding metabolomics to your multiomics framework reveals functional biology. Explore the use of DIABLO and PathIntegrate using our Multiomics and Microbiome Demo Data within our Integrated Bioinformatics Platform.
In This Webinar, We Cover:
- How metabolomics enhances multi-omics integration:
Learn why adding metabolite data provides the functional context that connects genes, proteins, and biological outcomes. - Using DIABLO to uncover cross-omic biomarkers:
See how DIABLO identifies correlated features across omics layers, revealing biologically coherent modules that improve prediction and interpretation. - Leveraging PathIntegrate for pathway-informed integration:
Understand how PathIntegrate uses known biological networks to connect omics features within pathways — strengthening causal inference and reducing noise in complex datasets. - Case studies demonstrating impact:
Explore published examples where integrating metabolomics with metagenomics, transcriptomics and proteomics uncovered new mechanistic hypotheses that would have been missed using a single-omic layer.
Speakers
Dr. Micaiah Ward , Ph.D.
Dr. Micaiah Ward is a Field Metabolomics Scientist supporting Metabolon’s North America East Region. Prior to joining Metabolon, Micaiah earned her Ph.D. in Cellular and Molecular Biology from Florida State University where her research incorporated genomics, transcriptomics and proteomics to investigate and characterize venoms from snakes, centipedes and scorpions. In addition, she used genome-wide association (GWAS) and evolve and resequencing (E&R) approaches to identify the genetic architecture of evolved venom resistance in fruit flies (Drosophila melanogaster). Micaiah then served as a Postdoctoral Research Fellow at Regeneron Pharmaceuticals, where she honed experience in immuno-oncology, infectious diseases, and CRISPR technology. Her broad scientific acumen and multi-omics expertise allows Micaiah to highlight the added value of metabolomics in moving the needle of scientific progress across basic and applied research areas in academia and industry.
Dr Joseph McGinley, Ph.D.
Joseph McGinley is a Principal Bioinformatician with 8 years of experience in bioinformatics and computational biology who is passionate about the application of these techniques to the improvement of healthcare.
Joseph has extensive experience designing, implementing and managing bioinformatics and machine learning approaches to disease characterisation and drug target discovery and has applied these approaches at Metabolon developing multiomics approaches for integrating metabolomics with other omics data.
References
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2. Surendran, P., Stewart, I.D., Au Yeung, V.P.W. et al. Rare and common genetic determinants of metabolic individuality and their effects on human health. Nat Med 28, 2321–2332 (2022). https://doi.org/10.1038/s41591-022-02046-0
3. Benedetti, E., Liu, E.M., Tang, C. et al. A multimodal atlas of tumour metabolism reveals the architecture of gene–metabolite covariation. Nat Metab 5, 1029–1044 (2023). https://doi.org/10.1038/s42255-023-00817-8
4. Singh A, Shannon CP, Gautier B, et al. DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics. 2019;35(17):3055-3062. doi:10.1093/bioinformatics/bty1054
5. Zhou, L., Surapaneni, A., Rhee, E.P. et al. Integrated proteomic and metabolomic modules identified as biomarkers of mortality in the Atherosclerosis Risk in Communities study and the African American Study of Kidney Disease and Hypertension. Hum Genomics 16, 53 (2022). https://doi.org/10.1186/s40246-022-00425-9
6. Bowerman, K.L., Rehman, S.F., Vaughan, A. et al. Disease-associated gut microbiome and metabolome changes in patients with chronic obstructive pulmonary disease. Nat Commun 11, 5886 (2020). https://doi.org/10.1038/s41467-020-19701-0
7. Lee, A.H., Shannon, C.P., Amenyogbe, N. et al. Dynamic molecular changes during the first week of human life follow a robust developmental trajectory. Nat Commun 10, 1092 (2019). https://doi.org/10.1038/s41467-019-08794-x
8. Wieder C, Cooke J, Frainay C, et al. PathIntegrate: multivariate modelling approaches for pathway-based multi-omics data integration. PLoS Comput Biol. 2024;20(3):e1011814. doi:10.1371/journal.pcbi.1011814
9. Byeon SK, Madugundu AK, Garapati K, Ramarajan MG, Saraswat M, Kumar-M P, Hughes T, Shah R, Patnaik MM, Chia N, Ashrafzadeh-Kian S, Yao JD, Pritt BS, Cattaneo R, Salama ME, Zenka RM, Kipp BR, Grebe SKG, Singh RJ, Sadighi Akha AA, Algeciras-Schimnich A, Dasari S, Olson JE, Walsh JR, Venkatakrishnan AJ, Jenkinson G, O’Horo JC, Badley AD, Pandey A. Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study. Lancet Digit Health. 2022;4(9):e632-e645. doi:10.1016/S2589-7500(22)00112-1
