Module Information
Course Delivery
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | 1 Hours In class blackboard test In class blackboard assessment | 40% |
| Semester Exam | 2 Hours Written Exam Written exam | 60% |
| Supplementary Exam | 2 Hours Written Exam | 60% |
| Supplementary Exam | 1 Hours Computer Exam | 40% |
Learning Outcomes
On successful completion of this module students should be able to:
Explain scientific concepts that underpin biological data
Analyse and interpret biological data using computational methods and algorithms
Draw conclusions from the computational analysis of data
Recognise the strengths and limitations of computational methods when applied to a biological data set
Critically evaluate existing research in Computational Bioinformatics, and demonstrate how this research may be applied appropriately to alternative application areas.
Brief description
This is an interdisciplinary module introducing state-of-the-art computational methods and algorithms used for for biological data analysis. In particular, the module focuses on creation, analysis and interpretation of "omics" data which has broad applications in Health, Biology and Biotechnology. Some examples are biomarker discovery for disease diagnostics and prognostics, plant and animal breeding, environmental monitoring for infectious diseases and production of industrial enzymes. The students will be gently introduced to biological concepts and terminology with no prior knowledge required and will have the opportunity to apply their computing skills to discover new knowledge.
Content
Bioinformatics technologies and methods used in generating and analysing "omics" data
Computational methods for DNA sequence alignment, genome assembly, gene detection, gene annotation, genomic variant analysis and protein structure prediction
Applications of computational bioinformatics from association mapping (from genotype to phenotype) and biomarker discovery to disease diagnosis and prevention
Shell scripting for creating data processing pipelines in Unix environment for high performance and cloud computing
Ethical issues surrounding retrieval and use of biological information
Module Skills
| Skills Type | Skills details |
|---|---|
| Adaptability and resilience | Interdisciplinary skills and knowledge |
| Co-ordinating with others | Practical sessions and in-class activities |
| Creative Problem Solving | Data analysis skills, algorithm and data structure skills. |
| Critical and analytical thinking | Formulating a research question and the application of computational methods to test hypotheses. |
| Digital capability | Programming and using computational tools. |
| Professional communication | Documenting code, report writing. |
| Real world sense | The applications of biological data mining |
| Reflection | Understanding the impact of biological data mining. |
| Subject Specific Skills | Using a computer and online tools. Readings from current scientific literature. |
Notes
This module is at CQFW Level 7