GNA5012 · Applied bioinformatics

Overview

With advancements in high-throughput data generation technologies, we are now able to generate incredible volumes of data from genome-scale experiments. Biologists need to work with this large volume of data in various digital forms to extract biological knowledge from it. Bioinformatics is an interdisciplinary field that deals with processing, analysis, and management of biological information using computer science and information technologies. This unit will assist you to develop essential bioinformatics skills and focuses on the practical use of bioinformatics methods and resources for the analysis of nucleotide and protein sequences, as well as results from omics studies, with emphasis on their evolutionary underpinnings and statistical foundations. You will explore the basic concepts underlying bioinformatics algorithms for assembly, alignment and pattern finding. You will gain experience in working with data from –omics studies, and learn data type-specific methods to perform gene/protein expression analysis, clustering, network analysis, and data visualisation.

Areas of study: Genetics and genomics

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

The Handbook lists no prerequisite, corequisite or prohibition for this unit.

Learning outcomes

1. Evaluate current techniques to generate genomic data and apply standard workflows to analyse them;
2. Investigate gene set enrichment, network analysis and data visualisation protocols;
3. Evaluate quality and diagnose issues with raw sequencing data and assembled genomes;
4. Perform basic computer programming with case studies involving DNA pattern finding;
5. Identify both small and large-scale genomic variants;
6. Demonstrate team work, scientific communication and peer to peer learning and feedback.

Workload

• Two hours of online teaching/material (pre-recorded lectures/lessons); • Four hours of computer lab practical or equivalent and • Seven hours of independent study per week

Student experience, not official rules. Nothing here changes what the Handbook says.