Integrative genomics to underpinning somatic evolution, tumour heterogeneity, and treatment resistance

Next-generation sequencing has revolutionised medical genetics, but limitations remain. Third-generation long reads improve de novo assembly, mapping, phasing, and identification of structural variants and transcript isoforms. Moreover, direct sequencing eliminates amplification bias while allowing detection of base modifications and inference of molecular function (e.g. promoter methylation).

Capitalising on these advances and addressing a key need for innovative computational method development in the area, our lab aims to leverage long-read and single-cell multi-omics to level up our understanding of the impact of variation on human genome function. In a leap towards truly personalised genomic medicine, we will advance both germline genetics and yield insights into tumour heterogeneity and evolution.

In particular, we develop computational approaches and collaborate on new experimental methods leveraging single-molecule multi-omics sequencing. By improving de novo assembly and mapping, long reads enable unbiased detection of variation while yielding valuable context information through phasing and isoform information. Moreover, direct sequencing allows the detection of base modifications, linking genotypes, isoforms and molecular phenotypes at the single-molecule level.

Capitalising on these advances, as well as those in single-cell and spatial techniques, we are developing the tools to comprehensively chart the variation in ‘omics layers and their interplay underpinning somatic evolution, tumour heterogeneity, and treatment resistance.

Experience in clinical (pathology) or translational research in the oncology field. Experience or keen interest in technologies and computational biology.

Medical background or training in the field of oncology and/or pathology will accelerate the integration of the MD in the project.