Bioinformatics
Bioinformatics Specialization
Overview
Bioinformatics and related fields constitute exciting research disciplines in their own right. In addition, they enable and support unique novel avenues of research in the life sciences. Successful applications going beyond basic research cover white, red, and green biotechnology. The rapid acceleration of technology development underpinning new experimental methods generating large amounts of data have further strengthened the role of bioinformatics in the life sciences. Together with modern approaches to statistics, machine learning, and modelling they revolutionize the way we view, examine, and interact with living systems both in the laboratory and in real world applications. Consequently, the Guiding Principles of the Bioinformatics Specialization of the Biotechnology Master at Boku are to provide
- a balanced overview and introduction of an extremely wide and heterogeneous field,
- a strong toolkit for exploring the field, including strengthened skills in computing and statistics,
- a knowledge of common applications in areas where Boku has a strong track record, and
- early opportunities for further specialization through free electives (Freifächer) and supervision of Master research theses.
Structure
Each Specialization has a number of lectures, exercises, and seminars that are required. Depending on the specialization, these are provided in 21-27 hours of lecture/contact time. In all specializations, they are equivalent to 28 ECTS points. These are complemented by free electives (Freifächer) and opportunities for research towards a Master thesis to allow for further specialization, and this is a particularly important aspect of training in such a diverse field. The Bioinformatics Specialization currently includes the below required courses (terms are suggestions only):
Term | Type / ID | Title (abbrev.) | h | ECTS | Institute / Research Group | Lecturers / PIs |
---|---|---|---|---|---|---|
1 | VS 791328 | Modern bioinformatics | 2 | 3 | (ring lecture) | Boku: Kreil, Leisch, Oostenbrink, Sykacek. UVie: Flamm, Haeseler, Rattei. |
2 | VU 851309 | Statistics with R | 2 | 2 | Leisch | Melcher. |
2 | UE 851319 | Introduction to programming | 2 | 3 | Leisch | Moder. |
2 | VU 851318 | Multivariate statistics | 3 | 3 | Leisch | Moder. |
2/3 | VU 791330 | Bioinf: sel. aspects | 3 | 4 | Kreil | Kreil, Labaj. |
2 | VU 791331 | Machine Learning & Pattern Recogn. | 3 | 4 | Himmelbauer | Sykacek. |
3 | VS 791018 | High-throughput sequencing | 3 | 3 | Himmelbauer | Himmelbauer. |
3 | VO 791355 | Introduction to metabolic modelling | 2 | 2 | Zanghellini | Zanghellini. |
3 | VU 894304 | Modelling & Simulation of Biomolecules | 3 | 4 | Oostenbrink | Oostenbrink, Graf, Pechlaner. |
sum | 23 | 28 |
Recommended opportunities for specialization include the below free elective courses (terms are suggestions only):
Term | Type / ID | Title (abbrev.) | h | ECTS | Institute / Research Group | Lecturers / PIs |
---|---|---|---|---|---|---|
2 | VU 791329 | Bayesian Data Analysis in the Life Sciences | 3 | 4 | Himmelbauer | Sykacek. |
2 | VU 791402 | Efficient Microarray Data Analysis w/ R/FSPMA | 1 | 1 | Himmelbauer | Sykacek. |
3 | VU 791015 | Expression profiling by next-gen. sequencing | 2 | 2 | Labaj | Labaj. |
3 | UE 791020 | Sequencing data analysis | 3 | 3 | Himmelbauer | Himmelbauer. |
... |