Note: This is an archived Handbook entry from 2014.
|Dates & Locations:|| |
This subject is not offered in 2014.
|Time Commitment:||Contact Hours: 36 hours, comprised of one 2-hour lecture and one 1-hour workshop per week. |
Total Time Commitment:
One semester programming or equivalent experience.
|Recommended Background Knowledge:||
One semester of computer programming or equivalent experience.
|Non Allowed Subjects:||None|
|Core Participation Requirements:||
For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.
It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability
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.
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 tomotif 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:
Hurdle requirement: To pass the subject students must obtain at least:
|Prescribed Texts:|| |
|Breadth Options:|| |
This subject is not available as a breadth subject.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
On completion of this subject students should have the following skills:
LEARNING AND TEACHING METHODS
The subject will be delivered through a combination of lectures, group discussion, and supervised laboratory. The assigned project work is also a key feature in the learning process.
INDICATIVE KEY LEARNING RESOURCES
Students will have access to lecture notes and audio recording of the lectures. Papers drawn from the current literature are posted to the LMS for each topic.
CAREERS / INDUSTRY LINKS
The subject provides an overview of computational genomics, and as such is a foundation for applied and research careers in bioinformatics. Guest lectures are given by practitioners in the field.
Master of Biomedical Engineering |
Master of Engineering in Distributed Computing
Master of Philosophy - Engineering
Master of Science (Bioinformatics)
Master of Science (Computer Science)
Master of Software Systems Engineering
Computer Science |
Master of Engineering (Biomedical)
Master of Engineering (Software)
Download PDF version.