Research Group Bioinformatics Hagenberg


Since 2001, researchers at University of Applied Sciences, Hagenberg, have conducted research and development in bioinformatics research projects in order to solve problems of life sciences. In close connection to the study programmes Medical and Bioinformatics (bachelor level) and Biomedical Informatics (master level) we have specialized in sequence analysis and integrative bioinformatics; we have successfully conducted several research projects (basic research projects funded by Austrian funding agencies as well as R&D projects co-financed by companies). Our main research topics are: 



Dr. Stephan Winkler has been the head of the Bioinformatics Research Group in Hagenberg since 2010; currently, the group consists of 12 members (professors at FH OÖ as well as research associates and practicants).

Research Goals

Cells & Molecules

Fluorescence microscopy is one of the core technologies for biomedical research and diagnostics. Statistical and images analysis, such as the automatic detection of objects and structures, the development of tracking motion characteristics and the automatic pattern identification, are an interesting and important challenge for biomedical researchers. Our members of the Bioinformatics Research Group in Hagenberg are working on projects that are focused on

  • detection of single molecules, fluorescently labeled patterns and background structures in microscopy images of various tissues,
  • research and development of tracking algorithms for characterizing motions of cells and molecules to gather more insights, e.g., in the development of diseases, and
  • statistical analyses and visualization of immunology data to gain more detailed information about cell interactions.

Protein Identification

Recently, a new era in mass spectrometers has been introduced, covering the ability to measure high accuracy and high resolution tandem mass spectra. To live up to this achievement also on the software side we developed the database search engine MS Amanda. MS Amanda is a new scoring system to identify peptides out of tandem mass spectrometry data using a database of known proteins. The most important features of MS Amanda are the following:

  • This algorithm is especially designed for high resolution and high accuracy tandem mass spectra.
  • MS Amanda is free of charge
  • It is multi-threaded
  • It can be integrated in the Thermo Proteome Discoverer, a software suite of Thermo Fisher Scientific
  • It is also available as standalone version
  • It can be downloaded at
  • It is able to outperform gold-standard algorithms Mascot or SEQUEST

Future developments of MS Amanda will even more concentrate on the informations still hidden in tandem mass spectra and try to gain new insights into peptide fragmentation to further improve peptide and protein identification of high resolution data.

Data Mining & Clinical Trials

Over the last decades, data mining (the art of discovering relevant information in large-scale data) has played an essential role in bioinformatics and for example helped to gain new insights in biological processes, to identify the causes of diseases, and to find ways how to cure them. At the Bioinformatics Research Group in Hagenberg we have conducted and are still working on several projects that are, e.g., dedicated to

  • the data based identification of relationships between blood parameters and cancer diagnoses,
  • the formulation of virtual tumor markers,
  • the assessment of the impact of medications and food ingredients,
  • the identification of impact factors of dementia (especially Alzheimer’s disease) and time series models for the progress of the disease, and
  • the automated identification of states of diseases using microscopy images of patients.

The HeuristicLab framework (, developed and maintained at the HEAL research group in Hagenberg, is used as the basis for most of these research projects. Clinical trials are another important field in biomedical research. Over the last years we have worked on clinical trials on the impact of preanalytics, drug effects, and genome wide association studies.