Technical University of Applied Sciences Wildau / High Perfomance Computing in Life Sciences

Key figures

• Focus of our research: solving problems from life sciences using mathematical-statistical-informatical approaches and a broad range of algorithms and methods
• These include, but are not limited to whole genome, transcriptome and epigenome analyses, differential gene expression analysis, modelling of gene-, protein- and metabolic networks, investigation of genomic structural variants (SVs) and biostatistical computation.
• integrate different types of data
• development of algorithms for high performance computing (e.g. machine learning) to allow the combination of large-scale datasets
• identification and prediction of markers for diseases or variations in phenotypes, which could address questions in personalized medicine

We offer

• Analysis of biological high throughput -omics data e.g.:
  • Genome, epigenome, transcriptome and proteome sequence analysis
  • Expression analysis
  • Gene (structure) annotation
  • Structural variants analysis
• Systems biology and network modelling
  • Gene regulatory networks
  • Metabolic networks
  • Signal transduction networks
  • Disease models
• Construction of Databases
• Development and optimization of algorithms for high performance computing and GPGPU architectures