The common objective of developing and applying bioinformatics tools to better understand cancer biology fosters close interaction between the groups inside the Division of Applied Bioinformatics and cooperation with the Computational Oncology group in the Division of Theoretical Bioinformatics.



Applied Predictive Modeling

The Applied Predictive Modeling team lead by Dr. Dilafruz Juraeva performs cross-platform analysis of high-dimensional data for disease gene prioritization, disease risk estimation, survival analysis, therapy stratification, and drug response. Models are established by classification techniques, inferential statistical analysis and machine learning (kernel methods) as well as pathway analysis.

Cancer Evolution

The Cancer Evolution team headed by Dr. Qi Wang is interested in investigating premalignant conditions, intratumor heterogeneity and tumor metastasis, by analyzing high-throughput sequencing and array data. We apply techniques such as subclone characterization, phylogeny inference, variant phasing or population sampling to reconstruct the evolutionary history of cancer under natural and therapeutic selection, which helps us to rationalize the development of metastasis and the failure of treatments. In addition, we use human tumor xenografts and mouse models of human tumors to improve our understanding of tumor progression.

Clinical Bioinformatics

The Clinical Bioinformatics team headed by Dr. Barbara Hutter specializes in applying next generation sequencing data analysis for personalized oncology. We place emphasis on establishing workflows for fast and reliable detection of actionable mutations in individual cancer genomes. Our experience with sequencing and mapping artifacts coined the term “NGS data pathology”. We also take into account the role of mutagenesis by viruses in characterizing druggable lesions. A main focus of our research is translational communication with clinicians. In order to facilitate targeted therapy recommendation, we generate comprehensive lists of the "druggable genome". Currently we perform analyses of clinical genome and transcriptome sequencing data for two precison oncology projects: the INdividualized Therapy FOr Relapsed Malignancies in Childhood INFORM registry and the DKFZ-HIPO Project H021 in the NCT MASTER program that is directed towards younger adults with advanced-stage cancer across all histologies.

Comparative Cancer Genomics

The Comparative Cancer Genomics team lead by Dr. Lars Feuerbach focuses on integrating sequencing and array data across tumor subtypes and patient cohorts. In these Pan-Cancer studies the similarities and differences in the interplay of epigenome, genome and transcriptome during carciogenesis are investigated. Furthermore, dataset from orthogonal experimental techniques are integrated. We contribute to two Pan-Cancer projects of the International Cancer Genome Consortium ICGC, where we investigate the impact of point mutations in regulatory regions on gene expression across 50 tumor types and alterations of telomere length and structure during tumor progression. Furthermore, we perform pan-prostate cancer analysis in the ICGC Early-onset Prostate Cancer consortium. Our methodological expertise for high-level analysis comprises algorithm development, specialized datastructures for data integration, data mining, compact visualization of complex information, and statistical modeling.


The HIPO team is affiliated with both the Division of Applied Bioinformatics and the Computational Oncology group in the Division of Theoretical Bioinformatics. We provide bioinformatics service for the Heidelberg Center for Personalized Oncology (DKFZ-HIPO). Our research interests comprise comparative cancer genomics, cancer evolution, and methods development for analysis of epigenomic data and visualization. As an example, the team interaction graph is created with the circlize package.

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