Bioinformatics and Omics Data Analytics

Junior Research Group Bioinformatics and Omics Data Analytics

Dr. Matthias Schlesner

Recurrent rearrangements in the genomes of pediatric medulloblastomas investigated in the ICGC PedBrain project. Green lines represent inter- or intrachromosomal translocations, blue lines deletions, red lines duplications, and black lines inversions. The outer circles indicate hotspots of single nucleotide variants and genomic breakpoints, respectively.
© dkfz.de

Omics technologies and in particular next-generation sequencing have tremendously changed the way biological systems are studied. These technologies produce comprehensive molecular profiles at high resolution, and thereby give detailed insights into molecular changes in human diseases like cancer.

The Bioinformatics and Omics Data Analytics Group explores such high-dimensional data to address questions in basic and translational cancer research. A major focus of our work is the analysis and interpretation of next-generation sequencing data. In interdisciplinary research projects with cancer biologists and clinicians we aim to understand cancer genomes and the alterations of cancer cells at other molecular layers like the epigenome and transcriptome. Our goals include the identification of driver alterations, the reconstruction of tumor evolution, and the identification of targetable lesions and predictive biomarkers. Furthermore, we support personalized oncology projects, for example in the frame of the Heidelberg Center for Personalized Oncology (DKFZ-HIPO), by performing tumor (sub-)classification based on molecular profiles and by predicting the effectiveness of drugs in individual tumors.

To enable the exploration of high-throughput data for cancer research, we develop methods for data analysis, visualization and integration. This includes methods for quality control, processing and analysis of next-generation sequencing data and more general purpose tools like a workflow framework to assist high volume data processing on compute clusters and R packages for enhanced data visualization. We employ statistical and machine learning techniques to identify patterns in large datasets, which are then correlated with biological or clinical properties to ultimately deduce biological mechanisms and causal relationships.

We have contributed to several projects in the International Cancer Genome Consortium (ICGC) and contribute to several working groups of the ICGC Pan-Cancer Analysis of Whole Genomes Project (ICGC PCAWG). Furthermore, we are part of the Heidelberg Center for Human Bioinformatics (HD-HuB) in the German Network for Bioinformatics Infrastructure (de.NBI).

FUTURE OUTLOOK

To better understand the perturbations in cancer cells we will make use of multi-omics data and take advantage of data generated using novel techniques like single-cell methods. We will develop new computational methods for the analysis of novel data types and for the integration of heterogeneous data. Finally, we plan to combine genomics, transcriptomics and epigenomics data with imaging data to identify genomic-imaging signatures representative for certain tumor properties like tumor aggressiveness.

Contact

Dr. Matthias Schlesner
Bioinformatics and Omics Data Analytics (B240)
Deutsches Krebsforschungszentrum
Im Neuenheimer Feld 280
69120 Heidelberg
Tel: +49 6221 42-2720

Selected Publications

  • Northcott P.A. et al. (2017) The whole-genome landscape of medulloblastoma subtypes. Nature, 547(7663):311-317.
  • Sahm F. et al. (2017) Meningiomas induced by low-dose radiation carry structural variants of NF2 and a distinct mutational signature. Acta Neuropathol, 134(1):155-158.
  • Gu Z. et al. (2016) Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics, 32(18):2847-9
  • Richter J. et al. (2012) Recurrent mutation of the ID3 gene in Burkitt lymphoma identified by integrated genome, exome and transcriptome sequencing. Nat Genet, 44(12):1316-20.
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