Description
Bioinformatics
| Module title | Bioinformatics |
|---|---|
| Module code | BIOM516 |
| Academic year | 2020/1 |
| Credits | 15 |
| Module staff | Dr Ron Yang (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 60 |
|---|
Description - summary of the module content
Module description
Research in the biological sciences is increasingly dependent on large datasets such as those generated by DNA sequencing and microarrays. This is also true for diagnostics and medicine. Analysis of these datasets requires a range of skills and knowledge drawn from computer science, physical sciences and mathematics and statistics as well as biological sciences. Bioinformatics is the discipline that integrates algorithms and methods from these disciplines to model biological systems and infer patterns hidden in complex data.This module requires you to have a working knowledge of the R programming language and statistical computing package. This is usually obtained via the BIO1333 and BIO2071 Biosciences modules. If you have no prior R knowledge you are encouraged to use the additional supporting documents on the module’s ELE page to learn R during guided independent study.
Module aims - intentions of the module
This module’s main aim is to help to equip the next generation of biological scientists with a sufficient working knowledge of bioinformatics methods and concepts such that they can understand and critically evaluate the computational methods used in cutting-edge genomics and other biomedical sciences. Where possible and appropriate, the application of these bioinformatics methods will be illustrated with biological or biomedical examples from the recent peer-reviewed scientific literature.
The module also aims to equip the biologist with sufficient comprehension of the subject to effectively communicate and collaborate with specialist bioinformaticians in handling, modelling, and analysing large scale biological data and as such will provide a foundation for those wishing to go on to postgraduate study in bioinformatics and related fields.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Perform basic analyses on large-scale biological data
- 2. Select proper data analysis tools to analyse biological data
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 3. Analyse biological data in a systematic way including data uploading, data organisation, data pre-processing, data modelling, data analysis, results summary and data analysis reporting
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 4. Communicate effectively arguments, evidence and conclusions using written means in a manner appropriate to the intended audience
- 5. Analyse and evaluate data with limited guidance
Syllabus plan
Syllabus plan
- Basic tools used by bioinformaticians: R programming
- Methods for genomics: homology alignment, assembly, annotation
- Machine learning components: density estimation, cluster analysis, classification analysis and regression analysis
Workshops to cover: sequence analysis, data visualisation, cluster, classification and regression analysis.
Learning and teaching
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 30 | 120 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching | 10 | Lectures |
| Scheduled Learning and Teaching | 20 | Workshops |
| Guided Independent Study | 120 | Guided reading of literature, literature research and revision |
Assessment
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Workshops | 10 hours | 1-5 | Oral |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Machine learning | 50 | 30 hours | 1-5 | Written |
| Genomics | 50 | 30 hours | 1-5 | Written |
Re-assessment
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Machine learning | Essay (3000 words) | 1-5 | August Ref/Def |
| Genomics | Essay (3000 words) | 1-5 | August Ref/Def |
Re-assessment notes
Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment. Each re-assessment coursework requires 30 hours for 3000 word essays.
Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to submit an essay. The mark given for a re-assessment taken as a result of referral will count for 100% of the final mark and will be capped at 50%. Each referral coursework requires 30 hours for 3000 word essays.
Resources
Indicative learning resources - Basic reading
Most of the concepts and methods are covered in these textbooks. However, we will also use examples from scientific journals such as Nature, Science, Genome Research, etc. and these materials will be provided via ELE.
- Zvelebil MJ and Baum JO, Understanding Bioinformatics, Garland Science, 2007 (Exeter library: 570.285 ZVE)
- Agostini M, Practical Bioinformatics, Garland Science, 2012 (Exeter library: 572.86330285 AGO)
- Duda RO, Hart PE and Stork DG, Pattern classification, Wiley-Interscience, 2000 (Exeter library: 001.534 DUD)
- Bishop C, Neural Networks for Pattern Recognition, Oxford University Press, 1996 (free pdf file online)
Indicative learning resources - Web based and electronic resources
Module has an active ELE page
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | BIO2092 Genomics and Introductory Bioinformatics (or BIO2093 Modern Theories of Evolution or BIO2087 Genomics and Biotechnology) |
| Module co-requisites | None |
| NQF level (module) | 7 |
| Available as distance learning? | No |
| Origin date | 30/11/2015 |
| Last revision date | 18/08/2020 |