- Code BIOL3157
- Unit Value 6 units
- Offered by Biology Teaching and Learning Centre
- ANU College ANU Joint Colleges of Science
- Course subject Biology
- Areas of interest Bioinformatics, Biology
This course has been adjusted for remote participation in Sem 2 2021 due to COVID-19 restrictions. On-campus activities will not be available.
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 genomic biology, it is also central to this course. Accordingly, the course includes short sections on computer programming using the Python and R programming languages. Topics covered will include techniques for sequence comparison, population and comparative genomics, and transcript analysis.
Upon successful completion, students will have the knowledge and skills to:
- Describe and apply a variety of methods in bioinformatics, including computer programming.
- Describe and evaluate current research procedures across a range of topics in bioinformatics.
- Evaluate and interpret current literature in areas of bioinformatic practice.
- Evaluate research methodology in the context of bioinformatic analysis of DNA sequence data.
- Demonstrate the ability to obtain quantitative results from mathematical and statistical models through analytical and computational methods.
- Assignments on five topics (20% ea) distributed throughout the semester including computer programming exercise (100) [LO 1,2,3,4,5]
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The expected workload will consist of approximately 130 hours throughout the semester including:
- Face-to face component which may consist of 2 x 2 hour computer labs per week (total 48 hours).
- Approximately 82 hours of self directed study which will include preparation for computer labs and other assessment tasks.
Students are expected to actively participate and contribute towards discussions.
To be determined
Requisite and Incompatibility
Assumed KnowledgeStudents are strongly encouraged to take an introductory computer science course, such as COMP1730 or COMP1100.
Tuition fees are for the academic year indicated at the top of the page.
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- Unit value:
- 6 units
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