Computational Genomics (COMP90016)

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 is a selective subject in the MSc (Bioinformatics) and is an elective in other courses. It can also be taken by PhD students and by undergraduate students, subject to the approval of the subject coordinator.

INDICATIVE CONTENT

This subject covers the computational analysis of several important forms of genomic data. Topics include computational resource management, reproducible research principles, genomics workflows, sequence alignment, genome annotation, parallel computing, metagenomics and single-cell sequencing. The subject domain rapidly progresses, and subject content is regularly revised and updated.

Practical work includes writing bioinformatics functions with Python code, accessing genomics data repositories and using popular command-line tools.