Thesis

Reducing the complexity of OMICS data analysis

DNA sequence analysis is a complex and time consuming process. While the amount of data produced that needs to be analysed rapidly increases, neither the available computational resources nor human resources grow fast enough to keep up with the analysis. The goal of this thesis is to approach both of those bottlenecks in DNA and RNA analysis in the context of diagnostics.
The basis of this thesis is the GensearchNGS project which was developed as a prototype during the author's Master project. Large parts of the prototype where rewritten and extended depending on the needs of the diagnostics laboratories, always with the goal to lower the technical and computational complexity. This is achieved through an intuitive user interface, hiding as much of the complexity from the user as possible, to increase the amount of people able to perform the analysis.
The other approach is to create more efficient algorithms, lowering the time to do the analysis, as well as lowering the infrastructure requirements. Parts of this effort have been released in the GNATY project, which is a collection of free tools to perform common bio-informatics tasks in a more efficient manner.
When better algorithms and optimized code reach their limits on what is possible to speed up the data analysis, we turn to distributed computing. This is achieved among other techniques with the POP-Java programming language, which has been vastly improved and extended over the course of this thesis.
The thesis is a collaboration between the institute iCoSys at the HES-SO and the Department of bioinformatics at the University of Würzburg.

Diploma works

Contact information

Beat Wolf,
ORCID: 0000-0002-9307-7212
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