Descripción
Subject area Bioinformatics and Cancer Genetics Subject area description Somatic mutation accumulation and clonal evolution are central processes in cancer development. Through large-scale whole-genome sequencing and other high-throughput approaches, these processes can be mapped in detail, providing insights into tumor initiation, progression, and treatment response. This position focuses on the application and development of bioinformatics methods for the analysis of genomic cancer data, including large public international datasets and locally generated patient-derived sequencing data. The project may involve, for example, studies of somatic mutational processes, clonal dynamics, tumor evolutionary history, or integrative analyses of genomic and clinical data. Analyses of non-cancer-related sequencing data may also be considered. The work will be carried out in a research environment with strong expertise in cancer genomics, computational biology, and translational research. Work published by the group in recent time includes: Ögren et al., Light-based footprinting of a eukaryotic genome. Science Advances 2026; Elliott et al., Mechanistic basis of atypical TERT promoter mutations. Nature Communications. 2024; Elliott et al. Base-resolution UV footprinting by sequencing reveals distinctive damage signatures for DNA-binding proteins. Nature Communications. 2023; Boström et al. Somatic mutation distribution across tumour cohorts provides a signal for positive selection in cancer. Nature Communications. 2022; Elias et al. Independent somatic evolution underlies clustered neuroendocrine tumors in the human small intestine. Nature Communications. 2021; Van den Eynden et al. Lack of detectable neoantigen depletion signals in the untreated cancer genome. Nature Genetics. 2019. Duties The postdoctoral researcher will apply and develop computational methods for the analysis of whole-genome sequencing and other NGS-based data from patients, including mutation calling and de