Computational Biology

Computational Biology / Bioinformatics

High-throughput analysis techniques became integral part of virtually all biomedical research fields. The DKFZ has established one of the largest facilities for genome sequencing in Europe, and scientists at DKFZ have accumulated petabytes of genomics and other omics data, with a still increasing data growth rate. 

A number of research groups develops and employs computational methods to analyse this unique data resource. Some of these groups focus exclusively on computational research, while others combine experimental and computational work. Current fields of work include both basic research like the characterization of tumors and other cells by whole genome or other omics analyses, analysis of cellular networks, and personalized oncology where personalized data from sequencing or other omics analyses is used for patient stratification and to improve therapy decisions. Methodologic challenges are for example the integration of heterogeneous data, scaling, and dealing with missing values (especially in the field of single cell omics).

Brors group

Applied Bioinformatics 

We use computational methods, statistics and machine learning to study cancer genomics and derive treatment predictions; this includes cancer epigenetics and computational oncoimmunology methods.

Proportion of this group's activities taken up by computational research: 100%

Diederichs group

RNA Biology & Cancer

Our division 1) analyzes lung adenocarcinoma at a multi-omics scale integrating transcriptomics, circular RNA, non-coding RNA, proteomics and phosphoproteomics data; 2) exploits tumor genomics data to find, catalog and characterize unusual and noncoding mutations in cancer; and 3) analyzes proteomics data to identify RNA-dependent proteins.

Proportion of this group's activities taken up by computational research: 30%

Goncalves group

Somatic Evolution and Early Detection

The Goncalves group develops and applies computational methods to genomic datasets to address questions in basic and translational cancer research.

Proportion of this group's activities taken up by computational research: 80%

Hübschmann group

​​​Pattern Recognition and Digital Medicine (PRDM) 

Our group focuses on the development of supervised and unsupervised pattern recognition algorithms and their applications to data sets from biology and medicine, including high dimensional omics data, as well as​ the derivation of biomarkers.

Proportion of this group's activities taken up by computational research: 100%

Jäger group

Clinical Bioinformatics (@ Division of Pediatric Neurooncology and Hopp Children's Cancer Center Heidelberg - KiTZ)

We focus on bioinformatics method development and application for optimized diagnosis and prognosis of pediatric cancer based on molecular data from high-throughput sequencing (WGS, WES, RNAseq, ChipSeq, WGBS, scRNAseq), as well as the analysis of possible mechanisms of tumorigenesis.

Proportion of this group's activities taken up by computational research: 100%

Klingmüller group 

Systems Biology of Signal Transduction

We examine key dynamic properties of biological systems by combining the generation of quantitative time-resolved data with mathematical modeling and thereby contribute to personalized treatment options in cancer.

Proportion of this group's activities taken up by computational research: 25%

Kopp-Schneider group

Biostatistics

One of the various research activities in the Biostatistics group is to develop prediction models for precision medicine using state-of-the-art machine learning and statistical approaches, and to identify causal effects in various contexts, such as time-to-event analysis and classification problems.

Proportion of this group's activities taken up by computational research: 80%

Lutsik group

Computational Epigenomics (@ Division of Cancer Epigenomics)

We are processing, analysing and interpreting high-throughput data from all epigenenomic layers, aiming at novel insights about epigenetic mechanisms of cancer and the establishment of tumor heterogeneity. We are also active in method development, with a particular focus upon DNA methylome deconvolution. Finally, we are interested in technical aspects of bioinformatics, such as comprehensive benchmarking of software tools, especially those relevant for the epigenomic research, and modern infrastructural solutions. 

Proportion of this group's activities taken up by computational research: 100%

Schlesner group

Bioinformatics and Omics Data Analytics

The Schlesner group develops and applies methods for data analysis, visualization and integration to explores omics data and address questions in basic and translational cancer research.

Proportion of this group's activities taken up by computational research: 100%

Stegle group

Computational Genomics & Systems Genetics

The Stegle team uses statistics and machine learning to analyse and integrate genomic variation datasets. 

Proportion of this group's activities taken up by computational research: 100%

Toprak group

Theoretical cancer multi-omics analysis methods research (@ Division of Neuroblastoma Genomics)

Our group focuses on the development of integrative analysis and interactive visualization tools for multi-omics, phenotype and clinical data using fast algorithms and modern web technologies. 

Proportion of this group's activities taken up by computational research: 100%

Zapatka group

Computational Cancer Genomics (@ Division of Molecular Genetics) 

Currently the main focus of the group is the understanding of biological processes in the context of clinical applications through the analysis of high throughput sequencing data.

Proportion of this group's activities taken up by computational research: 100%

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