|Year of offer||2019|
|Subject level||Graduate coursework|
|Fees||Subject EFTSL, Level, Discipline & Census Date|
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 construction, 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
INTENDED LEARNING OUTCOMES (ILO)
On completion of this subject the student is expected to:
- Describe and apply key algorithms used in the analysis of genomics data
- Describe next generation DNA sequencing, and compare and contrast it with the application of next generation sequencing to RNA (RNA-seq)
- Describe the application of machine learning techniques to gene expression data, and their strengths and weaknesses
- Understand the uses and limitations of bioinformatics software tools which use these algorithms, and apply these tools to practical data analysis
- Describe the limitations of current methods in functional genomics.
Having completed this unit the student is expected to have the following skills:
- Read the current literature in functional genome analysis
- Describe current research issues in computational analysis of functional genomics data
- Investigate current genomics software tools, understand their principles and limitations, and apply them appropriately.