THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2020/2021

Information in the Degree Programme Tables may still be subject to change in response to Covid-19

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DRPS : Course Catalogue : School of Biological Sciences : Postgraduate

Postgraduate Course: Metagenomics for Industrial Biotechnology (BITE11002)

Course Outline
SchoolSchool of Biological Sciences CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryIn Block 3, the course will provide an introduction to experimental design and data collection in the context of a laboratory experiment on biogas-producing reactors (anaerobic digesters) of relevance to industrial biotechnology. If necessary, this section can be delivered entirely via online classes and video demonstration, but ideally students will be able to attend the lab in person. In Block 4, data from the metataxonomic and metagenomic analysis of this experiment (mostly already collected in 2018-19) will be analysed via bioinformatics tools, which can if necessary be delivered entirely online. Assessment will be by 3 pieces of ICA.
Course description Industrial biotechnology generally involves the growth of microorganisms in reactors under defined conditions to produce a valuable product. This involves challenges of scale, environmental variation and reproducibility, all of which in turn challenge experimental investigations designed to enhance understanding and performance. The culture-independent approaches of metataxonomics and metagenomics provide fast, high-throughput methods to analyse the often-complex communities of microorganisms which inhabit these reactors, and permit multiple sampling regimes and statistical analysis to overcome the variability inherent in these complex systems. By combining these two elements, this course will teach a range of skills from basic experimental design, monitoring and data recording, through molecular techniques in DNA extraction and amplification, to advanced approaches in large-scale data analysis, bioinformatics and multivariate statistics.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2020/21, Not available to visiting students (SS1) Quota:  36
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 3, Supervised Practical/Workshop/Studio Hours 30, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 65 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Report 1 - Lab book assessment (20%). Early (Week 3) assessment of the students¿ recording of experimental set-up and data collection in a lab notebook.«br /»
Report 2 - Anaerobic digestion (40%). Mid-point (end of Block 3) assessment of the students ability to report scientific background and data analysis on the experiment. (2,000 words)«br /»
Report 3 - Metagenomics report (40%). End-course assessment of the students¿ skills in bioinformatic and statistical analysis of DNA sequence data in the context of a real-world industrial biotechnology problem. (2,000 words)«br /»
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Feedback Informal feedback via demonstrators and teaching staff while the course is in progress.
Comments on assignments in feedback sessions.
Course feedback form following completion.
Discussion with teaching staff after course completion.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Record laboratory procedures and results in a personal lab book.
  2. Understand operations and commercial opportunities in fermentation-based biotechnology.
  3. Demonstrate advanced knowledge of the importance of metataxonomics and metagenomics to biotechnology.
  4. Understand the basis of next- and third-generation sequencing technologies and how to apply them to marker gene and metagenomic analysis and analyse the results.
  5. Be able to produce a detailed report from in a concise scientific style describing the results of experimental procedures and data analysis.
Reading List
None
Additional Information
Graduate Attributes and Skills Generic cognitive skills: experimental planning; data evaluation; critical analysis of literature and experimental results.
Communication, numeracy and IT skills: report writing; statistical and numerical data analysis; command-line and specific bioinformatics skills.
Autonomy, accountability and working with others: individual literature searching and data analysis; team working in laboratory and computer-based sessions; delivery of an analytical report to an industrial-style brief.
KeywordsNot entered
Contacts
Course organiserDr Andrew Free
Tel: (0131 6)50 5338
Email: Andrew.Free@ed.ac.uk
Course secretaryMs Andrea Nichol
Tel: (0131 6)50 8643
Email: Andrea.Nichol@ed.ac.uk
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