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Semester 1 - Dual-Delivery
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The study of genomics is on the forefront of biology. Current laboratory technologies generate huge amounts of data and 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. You will learn the theory behind a variety of different approaches to genomic analysis, and be introduced to key tools in current use, preparing you to use existing methods appropriately as well as developing new ways to analyse genomic data. You will also have opportunities to apply your skills in workshops and assignments using both existing computational genomics tools and writing your own custom Python functions.
The subject is a core requirement in the MSc (Bioinformatics), and is an elective in other courses. It can also be taken by PhD students and by undergraduate students, subject to the approval of the subject coordinator.
This subject covers the computational analysis of several important forms of genomic data. Topics include computational resource management, reproducible research principles, genomics workflows, sequence alignment, genome annotation, parallel computing, metagenomics and single-cell sequencing. The subject domain rapidly progresses, and subject content is regularly revised and updated.
Practical work includes writing bioinformatics functions with Python code, accessing genomics data repositories and using popular command-line tools.
Intended learning outcomes
On completion of this subject the student is expected to:
- Use and manipulate a range of data formats used in computational genomics
- Identify and describe commonly used computational approaches to processing genomic data and appropriately apply them
- Discuss the advantages and disadvantages of a variety of algorithms that underpin computational genomic analyses
- Design analysis workflows for novel scenarios using tools and methods discussed in the subject
- Write simple Python programs and use programming libraries to complete computational genomics tasks
- Describe current research issues in computational genomics and related fields
- Explain the role of computational genomics in solving modern biological challenges
- Independent critical thought, rational inquiry and self-directed learning and research
- Using available resources autonomously to acquire relevant knowledge
- Ability to undertake problem identification, formulation and solution
- Research data management
- Capacity for creativity and innovation
- Ability to communicate effectively with related disciplines to solve multidisciplinary problems
Last updated: 19 November 2022