GHGA Lecture Series: Rolf Backofen (virtual)

  • 09 Jan 2024

Rolf Backofen from the University Freiburg talked about "The Freiburg Galaxy project" at the GHGA lecture series ("Advances in Data-Driven Biomedicine") on January 9, 2024.

 

Watch this talk here!

Biography:

Prof. Backofen studied computer science at the University of Erlangen, and received his Ph.D. in computer science from the University of Saarland in December 1994, where he worked at the German Research Center for Artificial Intelligence (DFKI). He received his habilitation from the University Munich (LMU) in February 2000. He was holding the chair for bioinformatics at the University of Jena from November 2001 till June 2005. After declining an offer for a full professorship at the University of Linz in 2004, became the holder of the chair for Bioinformatics at the University of Freiburg, Institute of Computer Science. His research interest include constraint programming, structure prediction in simplified protein models, investigation of protein energy landscapes, detection of RNA sequence/structure motifs, prediction and evaluation of alternative splice forms, description and detection of regulatory sequences. He is coauthor of the book "Computational Molecular Biology: An Introduction" (Wiley&Sons, Mathematical and Computational Biology Series, 2000).

Abstract:

The Freiburg Galaxy Project is part of the "German Network for Bioinformatics Infrastructure" (Deutsches Netzwerk für Bioinformatik­Infrastruktur, de.NBI) and the Collaborative Research Centre (CRC) 992 for Medical Epigenetics and offers within the RNA Bioinformatic Centre (RBC) a central platform for RNA analysis through the Galaxy platform.

Galaxy is an open source, web-based platform for data intensive biomedical research. It makes computational bioinformatics applications accessible to users lacking programming experience by enabling them to easily specify parameters for running tools and workflows. Galaxy provides access to a powerful analysis infrastructure through the web, and allows for reproducible and transparent data analysis.