University of Queensland
Area of Study
Taught In English
Pre-Requisites: SCIE2100 or MATH2210
Recommended Pre-Requisites: BIOL1020 + CSSE2002
Assumed Background: Familiarity with Python or other high-level computer programming language, basic statistics and probability, basic molecular biology.
Course Level Recommendations
ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.
Host University Units2
Recommended U.S. Semester Credits4
Recommended U.S. Quarter Units6
Hours & Credits
Advanced Bioinformatics equips the student with the interdisciplinary knowledge and skills necessary to meet the data-centred challenges of modern-day biology. Methods and algorithms for uncovering patterns in genomic data of different forms are discussed, and in several cases developed, implemented and applied to representative problems.
Modern-day biology is increasingly characterised by genome-scale and data-driven approaches. This trend has given rise to the discipline known as bioinformatics: the use of mathematics, statistics and computing to manage, analyse and build models from biological data to solve scientific problems. Present-day bioinformaticians are typically either bio-scientists armed with the methods of computer science, statistics and mathematics, or data analysts intimately acquainted with the nature and challenges of molecular biology. The course in Advanced Bioinformatics aims to cater for the background and needs of both these groups. Moreover, the course is designed to leverage synergies between the two groups. The course aims to instil
-An appreciation and understanding of a range of computational and statistical applications in biology involving the processing, analysis of and model-building from genomic data and other biological data
-The knowledge and skills (both theoretical and practical) required to develop computational methods and tools, to make informed, data-driven discoveries in molecular biology
In the context of the problems to which they are suited, the course presents key methodological concepts in bioinformatics. The course covers a set of problem themes, presented and supported by means of lectures, tutorials and practical work, including
1. biological sequence data and their analysis,
2. biological structure and discovering functional features,
3. systems biology, and
Teaching & Learning Activities allow the student to develop an in-depth technical understanding of common bioinformatics methods. Hands-on computer programming is used to reinforce understanding of bioinformatics algorithms, and to explore and evaluate their value on real data.Learning ObjectivesAfter successfully completing this course you should be able to:
Class Contact2 Lecture hours, 2 Practical or Laboratory hours
- Understand of the nature of and appropriate abstractions for different large-scale, data-centred biological problems
- Understand the scope and limitations of computational approaches for (a) the analysis of biological sequence data; (b) the discovery of structural and functional features from a range of data sources; and (c) the design and formulation of models of small-scale and large-scale biological phenomena at a molecular level
- Carry out project-based work in bioinformatics, utilising the iterative design process of observed biology and computational approach as an effective model for understanding biology, and accurately document such research-centred work
Courses and course hours of instruction are subject to change.
Eligibility for courses may be subject to a placement exam and/or pre-requisites.
Some courses may require additional fees.
Credits earned vary according to the policies of the students' home institutions. According to ISA policy and possible visa requirements, students must maintain full-time enrollment status, as determined by their home institutions, for the duration of the program.