The application of next generation sequencing and other high-throughput screening techniques for personalized oncology requires advanced bioinformatics methods for data analysis and the generation of interpretable results. HIPO bioinformatics is a collaborative effort mainly between the HIPO core bioinformatics team (N.Ishaque) and the Applied Bioinformatics division (B.Brors), the Computational Oncology and the Data Management groups in the Theoretical Bioinformatics division (R.Eils), and the Bioinformatics and Omics Data Analytics group (M.Schlesner). A major goal is the provision of standardized, state-of-the-art data processing and analysis workflows for all HIPO projects. Currently, the following workflows are offered for central and standardized data processing and quality assurance:
- alignment of whole genome, whole genome bisulphite, exome, ChIP and RNA sequencing data (and variants of these technologies)
- somatic variant calling of single nucleotide variants (SNVs) insertions and deletions (indels), structural variation (SVs), and copy number abberations (CNAs)
- methylation calling
Starting from sets of somatic variants for each tumor, extensive annotation will cover matches in databases of drug targets, known cancer driver mutations and germline predisposition variants. Interpretation of variant sets will rank those that are potentially therapeutically relevant and associate them to systemic cancer treatments. Incorporating oncological expertise, the evidence level for each candidate mutation will then be determined from literature and clinical trial information.
HIPO core bioinformatics team
The HIPO core bioinformatics team is responsible for quality assurance and primary data processing of all HIPO samples, as well as being actively involved deeper analysis in over 20 HIPO cancer cohort projects. In addition, the HIPO bioinformatics team is involved in the development of resources to facilitate the exploration of high dimensional data set, self learning cohort analysis tools, adoption of responsible and reproducible bioinformatics practices, and organizing training for clinical bioinformatics analysis together with the clinical bioinformatics team.