Postgraduate Course: Bioinformatics 2 (INFR11005)
|School||School of Informatics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|Summary||Bioinformatics is at the interface between two of the most influential scientific fields. An appreciation of computational and biological sciences, in particular the terminology employed in both fields, is essential for those working at such an interface. In this course, we aim to cover the following:
* The concepts of computer science that relate to problems in biological sciences.
* Commercial and academic perspectives on bioinformatics.
* The impact of bioinformatics on the methodologies used in biological science.
* The influence biological science has on computing science.
The course will cover the following:
* Next generation sequencing technologies
* Machine learning algorithms for sequence analysis
* Computational assembly of genomic sequences
* Gene finding
* Advanced functional genomics, expression analysis
* Industry guest lecture
* The future of bioinformatics: proteomics, neuroinformatics, e-science.
Relevant QAA Computing Curriculum Sections: Data Structures and Algorithms, Developing Technologies
Entry Requirements (not applicable to Visiting Students)
|| It is RECOMMENDED that students have passed
Bioinformatics 1 (INFR11016)
||Other requirements|| This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser.
This course assumes some mathematics at the level of undergraduate computer science, including:
- Probability theory, particularly discrete random variables;
- Basic statistics
No specific programming is taught or required, but students should be familiar with using software packages and will be required to use some packages in R for the lab/assignment.
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Not being delivered|
On completion of this course, the student will be able to:
- Describe the main computational algorithms used in the analysis of biological sequences
- Discuss the practical limitations of sequence analysis methods and contrast the methods available
- Appraise common biological data sources and the key contributing error/noise sources in such data
- Demonstrate an understanding of how experimental design in biology is critical to subsequent data analysis and representation in bioinformatics
- Critically evaluate research literature in the field
|Jones N.C. and Pevzner P. (2004) An Introduction to Bioinformatics Algorithms, MIT Press|
|Course organiser||Dr Ian Simpson
Tel: (0131 6)50 2747
|Course secretary||Ms Lindsay Seal
Tel: (0131 6)50 2701