Bioinformatics

Module titleBioinformatics
Module codeBIO3092
Academic year2019/0
Credits15
Module staff

Dr Ron Yang (Convenor)

Duration: Term123
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
  • 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 very limited 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
  • Cluster analysis
  • Classification analysis
  • Regression analysis

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)

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad
301200

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled Learning and Teaching10Lectures
Scheduled Learning and Teaching20Workshops
Guided Independent Study120Guided reading of literature, literature research and revision

Assessment

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Workshops10 hoursAllOral

Summative assessment (% of credit)

CourseworkWritten examsPractical exams
10000

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Sequence data analysis5030 hoursAllWritten
Machine learning5030 hoursAllWritten

Re-assessment

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

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Sequence data analysisEssayAllAugust Ref/Def
Machine learningEssayAllAugust 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.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 40%) 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 40%.

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

Key words search

Bioinformatics, next-generation sequencing, microarray, machine learning

Credit value15
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)

6

Available as distance learning?

No

Origin date

29/01/2014

Last revision date

04/03/2019