The vision is to integrate small organic molecule (the metabolome) analytical data with biobank big data. This will be achieved by building an annotated digital archive of biological samples from the Tromsø population study. We propose a radically new approach by facilitating bottom-up metabolomics with full metabolome component annotation. The project is initiated in the context of the UN sustainable development goal: ‘Ensure healthy lives and promote well-being for all at all ages’. We strongly believe that coupling the metabolome onto the vast digital archives of health and diseases status, genomics, and additional well-curated big data sets in the Tromsø study, can through focused efforts open up new scientific opportunities in data-driven research on diagnostic and lifestyle markers and lead to radical breakthroughs. The main novelties in the project include significant methodological advancements for a) rational storage and organization of metabolome big data, and b) development of a complete multi-parametric virtual analytical method to perform automated large-scale metabolome annotation and define the borders of investigated chemical space. This will require basic research on statistical machine learning combined with applied deep machine learning. The project will set new standards for accessibility of metabolome data to stakeholders and push the frontier for metabolomics in data-driven health research. This unique, well-organized, veracious, and readily retrievable digital archive will allow harnessing a bigger potential of Norway’s largest population study and increase its competitiveness. A work package is dedicated to dissemination to relevant stakeholders to establish familiarity with TROMBOLOME’s merits. The project group is international and cross-disciplinary with experts in machine learning, cheminformatics, and metabolomics with the complementary skills necessary to answer the research questions and realize the vision.
Project leader: Marie Mardal
Institution: Institutt for farmasi