Computational Genomics

AIM The study of genomics is on the forefront of biology. Current laboratory technologies generate huge amounts of data and computational analysis is necessary to make sense of these data. This subject covers a broad range of approaches to the computational analysis of genomic data. Students will learn the theory behind a variety of different approaches to genomic analysis, and be introduced to key tools in current use, preparing them to use existing methods appropriately as well as developing new ways to analyse genomic data. Students will also have opportunities to apply their skills in workshops and assignments using both existing computational genomics tools and writing custom Python functions. Computational Genomics can be taken as an elective subject. It can also be taken by undergraduate students, exchange students and PhD students, subject to the written approval of the subject coordinator.

• 1Use and manipulate a range of data formats used in computational genomics
• 2Identify and describe commonly used computational approaches to processing genomic data and appropriately apply them
• 3Discuss the advantages and disadvantages of a variety of algorithms that underpin computational genomic analyses
• 4Design analysis workflows for novel scenarios using tools and methods discussed in the subject
• 5Write simple Python programs and use programming libraries to complete computational genomics tasks
• 6Demonstrate understanding of current challenges in computational genomics and related fields
• 7Explain the role of computational genomics in solving modern biological challenges