Computational Genomics (COMP90016)
Graduate courseworkPoints: 12.5On Campus (Parkville)
About this subject
Contact information
Semester 1
Overview
| Availability | Semester 1 |
|---|---|
| Fees | Look up fees |
AIM
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. Students will learn the theory behind a variety of different approaches to genomic analysis, and be introduced to key tools in current use, preparing them to use existing methods appropriately as well as developing new ways to analyse genomic data. Students will also have opportunities to apply their skills in workshops and assignments using both existing computational genomics tools and writing custom Python functions.
Computational Genomics is a selective subject 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.
INDICATIVE CONTENT
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 the subject, students should be able 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
- Demonstrate understanding of current challenges in computational genomics and related fields
- Explain the role of computational genomics in solving modern biological challenges
Generic skills
- 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: 7 March 2025
Eligibility and requirements
Prerequisites
One of
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| COMP10001 | Foundations of Computing |
Semester 1 (On Campus - Parkville)
Summer Term (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
| COMP10002 | Foundations of Algorithms |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
| COMP20005 | Intro. to Numerical Computation in C |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
| COMP90041 | Programming and Software Development |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
| COMP90059 | Introduction to Programming |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
OR
Admission into or selection of one of the following:
- MC-SCICMP Master of Science (Computer Science)
- MC-CS Master of Computer Science
- MC-DATASC Master of Data Science
- MC-BIOMENG Master of Biomedical Engineering
- MC-SOFTENG Master of Software Engineering
- Software specialisation (formal) in the MC-ENG Master of Engineering
- Software with Business specialisation (formal) in the MC-ENG Master of Engineering
- Biomedical specialisation (formal) in the MC-ENG Master of Engineering
- Biomedical with Business specialisation (formal) in the MC-ENG Master of Engineering
Corequisites
None
Non-allowed subjects
None
Recommended background knowledge
Passed one semester of a Python programming subject; OR
Equivalent Python experience; OR
Extensive experience with another programming language.
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: 7 March 2025
Assessment
| Description | Timing | Percentage |
|---|---|---|
Programming Assignment 1
| From Week 4 to Week 5 | 10% |
Programming Assignment 2
| From Week 7 to Week 8 | 15% |
Programming Assignment 3
| From Week 10 to Week 11 | 15% |
Take-home programming examination
| During the examination period | 20% |
Written examination
| During the examination period | 40% |
Last updated: 7 March 2025
Dates & times
- Semester 1
Principal coordinator Steven Morgan Mode of delivery On Campus (Parkville) Contact hours 36 hours, comprised of two 1-hour lecture and one 1-hour workshop per week Total time commitment 180 hours Teaching period 3 March 2025 to 1 June 2025 Last self-enrol date 14 March 2025 Census date 31 March 2025 Last date to withdraw without fail 9 May 2025 Assessment period ends 27 June 2025 Semester 1 contact information
Time commitment details
200 hours
What do these dates mean
Visit this webpage to find out about these key dates, including how they impact on:
- Your tuition fees, academic transcript and statements.
- And for Commonwealth Supported students, your:
- Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.
Last updated: 7 March 2025
Further information
- Texts
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Science (Computer Science) Course Ph.D.- Engineering Course Master of Science (Bioinformatics) Course Master of Data Science Course Doctor of Philosophy - Engineering Course Master of Philosophy - Engineering 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.
Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants
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
Last updated: 7 March 2025