- 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
All activities that form part of this course will be delivered remotely in Sem 2 2020.
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:On satisfying the requirements of this course, 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.
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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WorkloadTwo computer labs of 2 hours per week. In addition, the course requires substantial number of self-assigned(i.e. non-contact) hours.
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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- Student Contribution Band:
- Unit value:
- 6 units
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