Introducing Splash & Ripple: Turning Metabolomics Data into Multiomic Mechanistic Insight

Metabolomics offers one of the most direct molecular views of phenotype, capturing the downstream effects of genetic and environmental influences across a high-dimensional, signal-rich dataset. For researchers considering metabolomics, the opportunity is substantial, but so is the interpretation challenge: a study can end with significant metabolites and enriched pathways without revealing the proteins involved, a plausible mechanism or the next biological question.

This webinar introduces Splash & Ripple, an interpretation approach designed to close that gap. Through a real-world case study, we will follow the journey from a complex metabolite profile to prioritised protein leads and coherent, evidence-linked mechanisms that were not apparent from the metabolite table alone. The example will distinguish what the metabolomics data show directly from what network analysis infers and what the literature supports. It will demonstrate how metabolomics can provide focused directions for experimental validation, translational research and integration with other omics, without requiring proteomics data as an input.

We will explain how the manually curated Metabolite Allied Proteins (M.A.P.) network, protein-ranking algorithm and IBP application work together. We will also demonstrate how Lead Scout, its traceable GenAI report and professional services help researchers move from a rich molecular signal to actionable biological insight.