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Technological advances in obtaining high throughput data have stimulated the development of new computational approaches to bioinformatics. This subject will cover core computational challenges in analysing bioinformatics data. We cover important algorithmic approaches and data structures used in solving these problems, and the challenges that arise as these problems increase in scale.
The subject is a core subject in the MSc (Bioinformatics) and is an elective in the Master of Information Technology and the Master of Engineering. It can also be taken by PhD students and by undergraduate students, subject to the approval of the lecturer.
The subject covers key algorithms used in bioinformatics, with a focus on genomics. Indicative topics are: sequence alignment (dynamic algorithms and seed-and-extend), genome assembly, variant detection, phylogenetic reconstruction, genomic intervals, complexity and correctness of algorithms, clustering and classification of genomics data, data reduction and visualisation.
The subject assumes you have experience in programming and familiarity with the foundations of genomics.
Intended learning outcomes
On completion of this subject the student is expected to be able to:
- Understand bioinformatics data representations
- Describe the computational challenges posed by common bioinformatics analyses
- Apply important algorithms used in solving bioinformatics problems
- Design and implement algorithms and data structures to address key questions in bioinformatics
- Design and implement a toolkit of algorithmic problem-solving techniques that can be applied to a diverse range of bioinformatics tasks
- Understand the feasibility constraints imposed by computational complexity of algorithms
- Learn techniques for extracting information from data and visualising the results of analyses
- Understand important algorithms in bioinformatics sufficiently well to implement simplified but functional tools
- Understand important algorithms in bioinformatics sufficiently well to understand their use in common bioinformatics software tools
- Investigate new bioinformatics software tools by reading the literature, understand their principles and limitations, and apply them appropriately
Last updated: 5 December 2019