EXE-iFIT: CE

Execellence cluster iFIT: Computational epigenomics project

01/08/2019 to 31/07/2022

Decades of cancer research, mostly driven through advances in molecular biology, have led to enormous insights into pathological mechanisms, as well as to knowledge about genetic and environmental factors that contribute to cancer development and progression. While these insights led to the elucidation of promising targets and build the basis for treatment strategies, many components of tumorigenesis remain elusive. Omics technologies, allowing comprehensive analyses of (epi)genomes, transcriptomes, metabolomes and proteomes of human cancers, have emerged as robust technologies over the past decade. The epigenetic level has been found to provide an important link between the static genome and the pathological cancer phenotype on the cellular level. In fact, several protagonists involved in the epigenetic regulation of gene expression, DNA repair, and DNA replication are mutated in a wide range of cancer types1,2. Recent findings from our own and our collaborator’s research (Zender lab, in collaboration with Bischof lab) have indicated the growing importance of epigenetic alterations in cancer development at the example of liver cancer3. Despite the importance of epigenetics in cancer research and prospectively in the clinical translation, large-scale repositories (e.g. ICGC, TCGA) are still dominated by genomic data and the robust and large-scale integration of epigenetics with other omics technologies has not been achieved. This project integrates expertise in cancer data integration4,5 and scalable bioinformatics6 with expertise in preclinical cancer research3. We will establish a computational framework that allows for rapid validation of newly generated experimental hypotheses through public data and scale-out bioinformatics building on nf-core6, our own software framework for FAIR data processing.
We will establish a computational framework that allows for rapid validation of newly generated experimental hypotheses through public data and scale-out bioinformatics with a primary focus on epigenetics.