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Technological advances in obtaining high throughput data from functioning cells have stimulated the development of new computational approaches to functional genomics and systems biology. This subject covers the theory and practice of the computational techniques used in genomics analysis, with an emphasis on functional genomics.
This 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 genomics analyses, and their application. Topics include: computational analysis of microarray data; classification and clustering, and their application to functional genomics analysis; detecting variants in genomic data; next generation sequencing for DNA; next generation sequencing for RNA.
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.
Last updated: 18 December 2020