Omic analyses for the biomedical sciences: from genomics to metabolomic BIOL5197
- Academic Session: 2022-23
- School: School of Infection and Immunity
- Credits: 20
- Level: Level 5 (SCQF level 11)
- Typically Offered: Semester 2
- Available to Visiting Students: No
- Available to Erasmus Students: No
An introduction to workflows for the resolution and characterisation of complex mixtures of biomolecules, from DNA to small molecule metabolites. The course will emphasize the potential and challenges of omic approaches and will include data handling tasks and demonstration.
Lectures, workshops and data analysis classes spread over 6 weeks
Students will prepare a mock grant proposal of 3000-3200 words, to address a specified biomedical question, using a combination of omic and reductionist/conventional approaches, and following a prescribed structure:
The course aims to provide students with a critical understanding of a range of modern "omics" technologies and applications. The course will introduce students to genomic, transcriptomic, proteomic and metabolomic techniques, and the analytical approaches that can be employed to examine the data output from these approaches. The relative benefits and challenges of each -omic approach will be presented, with exemplar data sets. Examples of the application of omic approaches in a variety of relevant biological systems will be presented, to give students an appreciation of the type of output generated and of typical strategies for data analysis and interpretation..
Intended Learning Outcomes of Course
By the end of this course students will be able to:
i) critically discuss information flow in biology, and evaluate the benefits of the different levels of omic data collection;
ii) discuss challenges to omic data collection;
iii) evaluate strategies to characterise a genome/transcriptome/proteome/metabolome;
iv) critically evaluate and summarise workflows that are exploited to identify, quantify and characterise molecules in a complex mixture;
v) explain the importance of validating proteomic data, and evaluate strategies available to do this;
vi) compare and contrast data from different "-omic" data collection approaches (genomics, transcriptomics, proteomics, metabolomics).
vii) Identify appropriate applications for different omic approaches.
viii) Design an experimental strategy to exploit an omic analysis
ix) Critically discuss the importance of controls and validation in 'omics strategies
x) Produce effective written communication in the form of a grant application
Minimum Requirement for Award of Credits
Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.