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The study of genomics is on the forefront of biology. Current laboratory technologies generate huge amounts of data. Computational analysis is necessary to make sense of these data. This subject covers a broad range of approaches to the computational analysis of genomic data. Students learn the theory behind the different approaches to genomic analysis, preparing them to use existing methods appropriately and positioning them to develop new ways to analyse genomic data.
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.
This subject covers computational analysis of genomic data, from the perspective of information theory. Topics include information theoretic analysis of genomic sequences; sequence comparison, including heuristic approaches and multiple sequence alignment; and approaches to motif finding and genome annotation, including probabilistic modelling and visualization, computational detection of RNA families, and current challenges in protein structure determination. Practical work includes writing bioinformatics applications programs and preparing a research report that uses existing bioinformatics web resources.
Intended learning outcomes
On completion of this subject the student is expected to:
- Describe and analyse critically the most commonly used computational approaches to processing genomic data and their theoretical underpinnings
- Describe current research issues in bioinformatics
- Outline a variety of algorithms used for processing genomic data and describe in some detail their operation and strengths and limitations
- Select algorithms appropriate to a given bioinformatics application
- Write simple bioinformatics computer programs and use bioinformatics programming libraries
- Describe the role of information theory in analysis of biological data.
On completion of this subject students should have the following skills:
- Ability to undertake problem identification, formulation and solution
- Ability to utilise a systems approach to complex problems and to design an operational performance
- Ability to manage information and documentation
- Capacity for creativity and innovation
- Ability to communicate effectively with both the engineering team and the community at large.
Last updated: 5 December 2019