Dr Matiss Ozols

Principal Bioinformatician

 
I specialise in computational genomics, proteomics, and single-cell multi-omics, building pipelines that scale to population cohorts. My research focuses on translating genetic variation into biological mechanisms, biomarkers, and drug targets, with applications in cardiovascular, immune-mediated, and dermatological diseases.

I am a computational geneticist dedicated to translating large-scale genomic and proteomic data into mechanistic insights and therapeutic opportunities. My expertise spans statistical genetics, proteomics, single-cell multi-omics, and AI/ML, underpinned by the development of scalable computational frameworks that enable discoveries at population and biobank scale. My overarching goal is to connect genetic variation to molecular pathways, biomarkers, and drug targets, accelerating the path from data to precision medicine.

A central focus of my work is the design and implementation of robust, production-grade pipelines that democratise access to advanced genomic analysis. I have played a leading role in developing QTLight for QTL mapping and YASCP for single-cell quality control and annotation. These frameworks are now applied across international cohorts, harmonising and analysing tens of thousands of samples to provide reproducible insights into disease biology. By linking genetic associations to molecular and cellular function, my work supports the identification of causal pathways and therapeutic targets at unprecedented scale.

Proteomics has been a defining theme throughout my career. During my PhD and subsequent research at Manchester, I pioneered computational and experimental strategies for biomarker and bioactive discovery. This led to the development of the Manchester Proteome platform, which underpinned the discovery of novel peptides incorporated into No7 Future Renew — a consumer health product that became one of Boots’ most successful launches. This translational success cemented my belief in the power of combining bioinformatics, AI, and experimental validation to deliver real-world impact. Building on this foundation, I now apply proteomics alongside statistical genetics and single-cell genomics to investigate cardiovascular, immune-mediated, and dermatological conditions, illuminating the molecular consequences of genetic variation across tissues and cell types.

My expertise spans both method development and integrative analysis. I am experienced in harmonising proteomics and genomics datasets, mass spectrometry analysis, pathway and network modelling, and functional genomics approaches. By combining large-scale data integration with advanced computational methods, including machine learning and deep learning, I aim to move rapidly from genetic associations to actionable biological hypotheses.

Equally important to me is fostering collaboration and mentorship. I have supervised PhD and MSc students, lectured in programming and bioinformatics, and contributed to international consortia focused on genetics, functional genomics, and ethical AI. I view science as a collective effort, and I thrive in environments where computational and experimental teams work closely together to accelerate discovery.

Looking ahead, my long-term vision is to harness multi-omics integration and AI to transform genomic insights into medicines. By identifying causal pathways, prioritising therapeutic targets, and informing patient stratification strategies, I aim to contribute to the development of precision therapeutics that deliver meaningful benefit for patients worldwide.

My timeline