In BIO214, protein structure, genomics, proteomics and NGS are delivered together with recent developments are covered. The biological and computational modules delivered in the first two years of BSc Bioinformatics program will be integrated in this module under bioinformatics framework to address various issues related to protein structure, genomics, proteomics and NGS. The lab sessions in BIO214 emphasise advanced programming skills to work with genomics datasets, such as transcriptome, proteome, functional genomics, etc.
The students are expected to get familiar with their biological background, mathematical
formulation, computer science implementation and hands-on practice. The students should:
1. Get familiar with key concepts related to structure, omics, and NGS
2. Understand the mathematical formulation of omic data processing
3. Be able to use computer science approach and bioinformatics software to solve practical problems related to protein structure and different types of omic data, especially proteomic data and NGS.
A Have a good understanding of the application of informatics techniques in protein structure, genomics, proteomics and NGS
B To highlight state-of-art research issues related to protein structure, genomics, proteomics and NGS.
C Analyse datasets of relevance to protein structure, genomics, proteomics and NGS, using tasks and workflows that will prepare students for contemporary and real research projects.
D Use local and web-based tools to conduct essential bioinformatics tasks, as required, for non-traditional or specialized task. To emphasise the need to keep up-to-date in the areas of genomics and biotechnology.
E To enable students to make reasoned decisions with bioinformatics techniques or further optimize a particular technique with respect to a specific bioinformatics problem.
F To enable students to address a biological problem using both bioinformatics approach and other approaches.
G Use popular main-stream bioinformatics resources to address well-defined problems such as structure prediction, etc.
H Demonstrate information literacy skills in the discovery, evaluation and acquisition of large high-throughput data as well as heterogeous biological datasets.
I Hands-on familiarity to the popular bioinformatics approaches by hand-calculation or on by computer programming.
1. Didactic component - the core of the teaching is lecture-based with Q/A and feedback.
2. Self-learning component - students are encouraged to read around the subject materials.
3. Comprehension/review exercise - two continuous assessments, following supervised discussion and Q/A sessions in the seminars.
4. Case studies will be supplied to help students place the course material in context.
5. Working in computer lab with bioinformatics tools to solve practical problems.