Novel bioinformatics approaches and software
We develop approaches that balance and inter-connect quantitative and biological knowledge. Many acknowledge that no field has generated higher expectations, deeper frustrations, and more translation anxiety than advances in human genomics. Early on, we — among others — highlighted the role of rigorous epidemiological and statistical approaches in improving the prospect of genomics and big data in personalized medicine. Our approach, termed Systems Epidemiology (Lund & Dumeaux, 2008 CEBP), proposes to integrate human -omics data with measurements from observational epidemiologic studies to better characterize the diverse range of factors influencing complex diseases, and help infer causation and support evidence-based research (Lund & Dumeaux, 2010 Int J Epi). In line with these concepts, we supported the development of a large biobank within the Norwegian Women and Cancer Study (Dumeaux et al., 2008 BMC).
Critical to these efforts is the development of computational methodologies that support the integration and interpretation of complex real-life data. Specifically, we have developed novel methodologies for:
- the sensitive detection of low-amplitude changes in blood profiles across healthy individuals (developed in PLoS Genetics 2010);
- identifying genes, pathways, and processes that co-vary and interact across tissues and environments;
- predicting activation of molecular pathways in a single-patient manner satisfying clinical-practice constraints associated with personalized medicine;
- as well as additional methodologies within collaborative manuscripts (Huttenhower et al., 2009 Genome Research; Barutcuoglu et al., 2009 Bioinformatics; Bettauer et al., 2022 Microbiology Spectrum).
Most recently, we have extended this methodological program to FFPE-grade transcriptomics (PREFFECT) and to microbial functional archetypes (deep-fMC) — two settings where the gap between what is technically measurable and what is clinically actionable demanded new tooling.
Selected papers
- Generative and integrative modeling for transcriptomics with formalin-fixed paraffin-embedded material
- Detecting gene-signature activation in breast cancer in an absolute, single-patient manner
- Building applications for interactive data exploration in systems biology
- A deep learning approach to capture the essence of Candida albicans morphologies
- Reproducible data analysis pipelines for precision medicine
Selected software
- MIxT — multi-tissue transcriptional integration.
- PREFFECT — generative modelling for FFPE RNA-seq.
- Candescence — C. albicans morphology classifier.
- deep-fMC — functional microbial configurations from gut metagenomes.