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Computational Genomics (COMP90016)
Graduate courseworkPoints: 12.5On Campus (Parkville)
For information about the University’s phased return to campus and in-person activity in Winter and Semester 2, please refer to the on-campus subjects page.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 1
Vicky Perreau
Please refer to the LMS for up-to-date subject information, including assessment and participation requirements, for subjects being offered in 2020.
Overview
Availability | Semester 1 |
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Fees | Look up fees |
AIM
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.
INDICATIVE CONTENT
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.
Generic skills
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: 3 November 2022
Eligibility and requirements
Prerequisites
One of the following:
COMP90059 COMP10001 COMP10002 COMP20005 COMP90041
Or entry into one of the following courses:
- Master of Engineering (Biomedical)
- Master of Engineering (Biomedical with Business)
- Master of Engineering (Software)
- Master of Engineering (Software with Business)
- Master of Science (Computer Science
- Master of Computer Science
Corequisites
None
Non-allowed subjects
None
Recommended background knowledge
One semester of computer programming or equivalent experience.
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 3 November 2022
Assessment
Due to the impact of COVID-19, assessment may differ from that published in the Handbook. Students are reminded to check the subject assessment requirements published in the subject outline on the LMS
Description | Timing | Percentage |
---|---|---|
Programming Assignment 1, requiring approximately 13 - 15 hours of work, addresses Intended Learning Outcomes (ILOs) 5 and 6
| Week 4 | 10% |
Programming Assignment 2, requiring approximately 13 - 15 hours of work, addresses Intended Learning Outcomes (ILOs) 5 and 6
| Week 6 | 10% |
Programming Assignment 3, requiring approximately 13 - 15 hours of work, addresses ILOs 5 and 6
| Week 9 | 10% |
Programming Assignment 4, requiring approximately 13 - 15 hours of work addresses Intended Learning Outcomes (ILOs) 5 and 6
| Week 11 | 10% |
A written examination addresses ILOs 1-4 & 6
| End of semester | 60% |
Additional details
Hurdle requirement: To pass the subject students must obtain at least: 50% overall 30/60 on the end-of-semester examination 20/40 on the project work.
Last updated: 3 November 2022
Dates & times
- Semester 1
Principal coordinator Vicky Perreau Mode of delivery On Campus (Parkville) Contact hours 36 hours, comprised of one 2-hour lecture and one 1-hour workshop per week Total time commitment 200 hours Teaching period 2 March 2020 to 7 June 2020 Last self-enrol date 13 March 2020 Census date 30 April 2020 Last date to withdraw without fail 5 June 2020 Assessment period ends 3 July 2020 Semester 1 contact information
Vicky Perreau
Time commitment details
200 hours
Last updated: 3 November 2022
Further information
- Texts
Prescribed texts
None
- Subject notes
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 on 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.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Science (Computer Science) Course Master of Data Science Course Master of Science (Bioinformatics) Course Doctor of Philosophy - Engineering Course Ph.D.- Engineering Course Master of Philosophy - Engineering Major Computer Science Specialisation (formal) Biomedical with Business Specialisation (formal) Software Specialisation (formal) Biomedical - Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
Additional information for this subject
Subject coordinator approval required.
- Available to Study Abroad and/or Study Exchange Students
This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.
Last updated: 3 November 2022