Units / GEN3010
GEN3010 · Applied bioinformatics
2026 Handbook6 credit pointsLevel 3School of Biological Sciences
Last checked: 23 Aug 2026 UTCOverview
With advancements in high-throughput data generation technologies, modern biology is becoming increasingly quantitative in nature. Biologists now commonly deal with large volumes of heterogeneous data in various digital formats. Bioinformatics is the 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 bioinformatic algorithms for assembly, alignment and pattern finding. You will also get hands-on 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 visualization.
Areas of study: Genetics and genomics
Offerings
The Handbook publishes no offerings for this unit.
Assessment
The Handbook lists an examination for this unit.
| # | Assessment | Type | Weight | Hurdle |
|---|---|---|---|---|
| 1 | Computer lab | Demonstration | 40% | — |
| 2 | Programming assignments | Demonstration | 5% | — |
| 3 | Group project | Project | 15% | — |
| 4 | Final assessment - Exam (2 hours and 10 minutes) | Examination | 40% | — |
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
- Analyze current high-throughput techniques to generate omics data and apply standard workflows to analyze them.
- Implement gene set enrichment, network analysis and data visualization protocols.
- Evaluate genome assemblies and underlying algorithms using short and long read sequencing data.
- Perform basic computer programming with case studies involving DNA pattern finding.
- Describe applications of machine learning in biology.
- Demonstrate team work, scientific communication and peer to peer learning and feedback.
Workload
Two 1-hour lectures and one 3-hour computer lab practical per week or equivalent Seven hours of independent study per week
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