Computational Bioinformatics

On successful completion of this module students should be able to:

1. Explain scientific concepts that underpin biological data
2. Analyse and interpret biological data using computational methods and algorithms
3. Draw conclusions from the computational analysis of data
4. Recognise the strengths and limitations of computational methods when applied to a biological data set
5. Critically evaluate existing research in Computational Bioinformatics, and demonstrate how this research may be applied appropriately to alternative application areas.

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.

Data sets and knowledge representation in biology (e.g., the basics of DNA, RNA and protein sequences)

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

This module is at CQFW Level 7