Mathematical Sciences Institute · ANU College of Systems and Society
This course begins with a brief review of some of the areas of probability and statistics needed for applications to bioinformatics problems. Typical problems addressed by bioinformaticians are identifying functionally different parts of a genome, searching DNA or protein databases to find sequences which are functionally similar to a given query sequence, or inferring the relatedness of different species by measuring the similarity of their genomes. The course will cover the mathematical theory behind algorithms commonly used by biologists and also give examples of current research. Furthermore, in consultation with the course lecturer, students will (i) select a research topic related to this course, and through reading of professional articles, acquire a fundamental knowledge of that topic. (ii) Write a report (2500 word limit) on the selected topic, explaining fundamental concepts and highlight key questions currently researched in the field. (iii) Demonstrate effective oral communication skills by presenting complex concepts to staff and other students in a 20-minute seminar, based on the report, as well as answer 10 minutes of questions. Note: This course is co-taught with undergraduate students but assessed separately.
Assignments
Learning outcomes 1, 2, 3
Final exam
Learning outcomes 1, 2, 3
A research project based on a specific example from published scientific literature Regardless of performance on other assessment items, a minimum score of 35% on the research project is required to pass the course. (This is known as a 'course hurdle'.)
Learning outcomes 4, 5, 6, 7
The expected workload will consist of approximately 130 hours throughout the semester including: - Face-to face component which will consist of 3 x 1 hour Lectures and 1 x 1 hour workshop per week. - Approximately 82 hours of self-directed study which will include listening/viewing the online lectures, preparation for the weekly lectures, workshops and other assessment tasks.
None.