Multi-omics and exposome data integration for environment and health research

Datharsis, a University of Granada spin-off specialised in explainable data analysis, is looking to join a consortium as the partner responsible for data integration and analysis, either leading the data analysis work package or contributing specific tasks.

Characterising the exposome means combining data that do not naturally fit together: molecular profiles, clinical records, climate and environmental measurements, across individuals, seasons and regions. The difficulty is not only integrating them, but showing which exposures drive which health effects, and separating that signal from everything else varying at the same time. That is exactly what our methods do.

What we offer:

• Study and sampling design across cohorts, sites and seasons, from the proposal stage

• Integration of omics layers (metabolomics, proteomics, transcriptomics, epigenomics, metagenomics, spatial omics) with clinical, climate and environmental exposure data

• Multifactor models (ASCA, vASCA, group-wise models) that separate the effect of each exposure and their interactions, and identify the molecules behind each effect

• Models that separate shared trends from site- and individual-specific variability

• Rigorous validation with cross-validation and permutation testing

• Reproducible, auditable, FAIR-aligned analysis pipelines

• Access to DathaSuite, our analysis platform, and training for consortium researchers

Our team’s methods have been applied to the effect of contaminants across multiple tissues in ecotoxicology, warming trends in high mountain lakes separated from site-specific features, and multi-omics profiling of a paediatric cohort. See these and other documented cases.

We are looking for consortia in environmental health, epidemiology, exposure science and climate research that need a partner to design the study and make the data speak.