Description

Bioinformatics

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.

The skills you gain from lectures, practicals, readings and seminars will develop or enhance your employability. Transferable skills to other sectors include: problem solving (linking theory to practice, responding to novel and unfamiliar problems, data handling), time management (managing time effectively individually and within a group), collaboration (taking initiative and leading others, supporting others in their work), self and peer review (taking responsibility for own learning, using feedback from multiple sources) and audience awareness (presenting ideas effectively in multiple formats).

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 such as sequencing data and microarray expression data
• 2. Select proper data analysis tools to analyse biological data
• 3. Explain how relational databases can be used for biological data exchange

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

• 4. 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
• 5. Combine multiple data analysis tools for comprehensive biological data analysis

ILO: Personal and key skills

On successfully completing the module you will be able to...

• 6. Communicate effectively arguments, evidence and conclusions using written and oral means in a manner appropriate to the intended audience
• 7. Analyse and evaluate appropriate data with minimal guidance

Syllabus plan

Syllabus plan

• Basic tools used by bioinformaticians: The Unix/Linux, programming, and databases
• Methods for sequence analysis: alignment, assembly and functional prediction
• Density estimation for gene expression data
• Cluster analysis for gene expression data
• Classification analysis for gene expression data
• Regression analysis for gene expression data
• Systems biology - differential equations and difference equations

Workshops to cover: sequence analysis, expression data cluster analysis, data classification and regression analysis.

Learning and teaching

Learning activities and teaching methods (given in hours of study time)

Details of learning activities and teaching methods

Assessment

Formative assessment

Summative assessment (% of credit)

Details of summative assessment

Re-assessment

Details of re-assessment (where required by referral or deferral)

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.

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%.

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)

Indicative learning resources - Web based and electronic resources

• ELE page: http://vle.exeter.ac.uk/course/view.php?id=4493

Module has an active ELE page

Key words search

Bioinformatics, next-generation sequencing, microarray, machine learning