This course provides an introduction to the key methods and technologies of bioinformatics as pertinent to genomics. These are the fastest growing fields of biology and perhaps science.
Bioinformatics is a rapidly growing scientific discipline at the interface of genomics, statistics and computer science that has distinct but overlapping aspects: the development of computational infrastructure (eg. algorithms, programs, databases) and their use to analyse a wide variety of biological data. Among these data, genes, transcripts and epigenetic features play a central role. Their rapid and large-scale acquisition in today's genomics, transcriptomics, proteomics and other -omics projects poses the major challenge of modern biology. The large-scale and genome-wide analysis of these data relies on advances in bioinformatics and statistics.
As computer literacy is central to bioinformatics, it is also central to this course. Accordingly, the course includes short sections on computer programming using the Python and R programming languages. We further cover advanced work practices employed during bioinformatics research, including code testing and use of version control systems. Research topics covered will include techniques for sequence comparison, population and comparative genomics, and transcript analysis.
Note: Graduate students attend joint classes with undergraduates but are assessed separately.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
On satisfying the requirements of this course, students will have the knowledge and skills to:
- Describe and apply a variety of sophisticated work practices in bioinformatics, including computer programming.
- Describe and evaluate current research procedures across a range of advanced topics in bioinformatics.
- Evaluate and interpret current literature in areas of bioinformatic practice.
- Design, implement and critically evaluate research methodology in the context of advanced bioinformatic analysis of DNA sequence data.
- Demonstrate the ability to construct and evaluate hypotheses about genomic data from mathematical and statistical models through analytical and computational methods.
Indicative Assessment
Assessment will be based on:
Assignments on five topics 100% (20% ea) distributed throughout the semester including computer programming exercise - LO1,2,3,4,5.
In response to COVID-19: Please note that Semester 2 Class Summary information (available under the classes tab) is as up to date as possible. Changes to Class Summaries not captured by this publication will be available to enrolled students via Wattle.
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Workload
Two computer labs of 2 hours per week. In addition, the course requires a substantial number of self assigned(i.e. non-contact) hours.Requisite and Incompatibility
Assumed Knowledge
The equivalent of BIOL2151 or BIOL3161, and some statistical knowledge (equivalent to BIOL2001 or BIOL2202 or STAT1003 or STAT1008). Some previous experience in compute rprogramming will be an advantage.Fees
Tuition fees are for the academic year indicated at the top of the page.
If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are an undergraduate student and have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). You can find your student contribution amount for each course at Fees. Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2020 | $4050 |
- International fee paying students
| Year | Fee |
|---|---|
| 2020 | $5760 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
Second Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 8704 | 27 Jul 2020 | 03 Aug 2020 | 31 Aug 2020 | 30 Oct 2020 | In Person | View |