Bioinformatics 2 (10 points) (P00861)

? Credit Points : 10 ? SCQF Level : 11 ? Acronym : INF-P-BIO2

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.

Entry Requirements

? Pre-requisites : Bioinformatics 1 (P00859) For Informatics PG students only, or by special permission of the School. This course assumes an undergraduate degree in computing or mathematical sciences. However, it would also be suitable for an individual from biological sciences with some programming experience.

? Prohibited combinations : Bioinformatics 2 (20 pts)

Subject Areas

Home subject area

Bioinformatics, (School of Informatics, Schedule O)

Other subject areas

Database Systems, (School of Informatics, Schedule O)

Neuroinformatics, (School of Informatics, Schedule O)

Computational Systems Biology, (School of Informatics, Schedule O)

Life Science Computing, (School of Informatics, Schedule O)

Delivery Information

? Normal year taken : Postgraduate

? Delivery Period : Semester 2 (Blocks 3-4)

? Contact Teaching Time : 2 hour(s) per week for 10 weeks

First Class Information

Date	Start	End	Room	Area	Additional Information
09/01/2008	11:10	12:00	Room 4.18, David Hume Tower	Central

All of the following classes

Type	Day	Start	End	Area
Lecture	Wednesday	11:10	13:00	Central

Summary of Intended Learning Outcomes

It is anticipated that students who successfully complete the course 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