External summary

Much of modern biological science is based on the acquisition and analysis of large datasets of information, be they from genomics, large scale surveys or a collation of years of experimental work. Analysis of these data has given rise to a vigorous hybrid field, Bioinformatics, which aims to apply the best practice and insights from computational sciences to identifying and quantifying meaningful, biological signal in the data.

The MSc in Bioinformatics will train students in, and give students hands-on experience of, modern bioinformatics research themes and methods. Although the programme is based in Biological Sciences the programme has close links with Informatics, and so offers students a wide range of courses and approaches to the complex problems of informatics as applied to biology.

The programme is constructed round of a set of recommended courses focused on programming skills, statistical analysis and database science as well as bioinformatics. Additional optional courses allow students to specialise in several aspects of bioinformatics, from programme design and implementation to structural biology, drug discovery, systems biology and genomics. Students will be trained also in transferable skills, including group work, research planning and management.

Students, exposed to both practical and theoretical concepts in the programme, will be able to progress to advanced graduate study, academic research or industry with a portfolio of skills developed on the programme, including:

• a working knowledge of a programming language;
• a theoretical and practical grounding in the science of databasing;
• familiarity with the major tools and algorithms of bioinformatics;
• an understanding of modern data analysis techniques applied to  microarray and high throughput sequencing data
• independent research skills, gained through an individual major project;
• essential transferable skills (writing scientific texts, presentations, IT).

On graduation from the programme we expect that students will be able to:

• compete successfully for the best PhD positions and progress immediately to mature PhD research;
• progress to a research assistantship or other post in an academic biological- sciences, clinical or commercial laboratory, and set up and perform advanced bioinformatics services and research;
• Work in an academic, clinical or commercial bioinformatics service
• maintain their working knowledge of advanced bioinformatics and apply their skills in either academic, clinical or commercial settings.

Educational aims of programme

The MSc aims to give students a thorough grounding in Bioinformatics, and produce a trained intellect that is capable of critical thinking.

The programme aims to develop skills in:

• Knowledge and understanding
• Research skills in both authentic bioinformatics-related problems and library
• Awareness of unresolved issues and unanswered questions in the area of Bioinformatic
• Practical problem solving skills using computers- including  computer programming, system administration and data analysis
• Graduate attributes: a range of generic transferable skills

Programme outcomes: Knowledge and understanding

• A clear overview of the theoretical and intellectual basis of modern bioinformatics
• An appreciation of the roles of computational and experimental research in biology
• A wide experience of algorithms, programs and approaches to solving bioinformatics problems

Programme outcomes: Graduate attributes - Skills and abilities in research and enquiry

Through a combination of solving realistic problems, research projects and group work, students learn current skills and approaches in biological research.  An understanding of scientific method, allied to the ability to construct alternative arguments and hypotheses leads our students to develop an ability to evaluate evidence for and against particular points of view.  Our students will have developed numerical competence.  They will learn to report research data and conclusions through written reports and competent oral presentations.  They will develop strong computational skills and be familiar with operating computers to solve small and large scale computational tasks.

Through participation in a combination of different teaching and practical experiences, graduates acquire the ability to:

• Develop critical thinking
• Discuss and evaluate scientific arguments
• Exchange ideas with scientific colleagues
• Communicate concepts and ideas to the wider public
• Formulate scientific questions and programmes of research
• Ability to design, interpret and critique experiments

Programme outcomes: Graduate attributes - Skills and abilities in personal and intellectual autonomy

The development of critical thinking lies at the core of the intellectual training provided in the Bioinformatics MSc programme.  Students develop an increasing competence to deal with intellectual concepts and scientific discussion, and to evaluate contradictory arguments through both essay writing and computer research. 

Students acquire the ability to:

• Organise complex arguments and draw these together into a coherent conclusion
• Understanding the relative value of different scientific approaches
• Summarise and interpret the work of others in the context of previous work and likely developments.
• Evaluate the strength and weaknesses of scientific evidence, thereby being able to arrive at independent conclusions
• Analyse graphs figures and tables
• Practise and record accurate observation
• Deliver presentations in a logical and coherent manner
• Acquire knowledge of opportunities and career pathways for professional development
• Learn analytical methods and apply them to problem solving
• Consider and understand scientific theories
• Formulate, investigate and discuss questions
• Engage and draw on an understanding of scientific investigations
• Build on existing knowledge to suggest new directions for investigation
• Understand the relevance and importance of explaining scientific ideas and the impact of science to the wider community.
• Solve complex problems using computers

Programme outcomes: Graduate attributes - Skills and abilities in communication

The development of communication skills occurs at all stages of the MSc programme. Skills comprise of:

• Oral and written communication (project, poster and paper presentations)
• Computer skills
• Graphical and numerical skills
• Library skills
• Problem solving skills
• Group and teamwork skills (working effectively)
• Analytical skills
• Independent learning
• Time management
• Organisational skills
• Numeracy and statistical analysis skills
• Problem solving
• Online/Internet information retrieval skills

Programme outcomes: Graduate attributes - Skills and abilities in personal effectiveness

Student personal development is achieved through a number of interconnected learning processes and interaction with other students and staff. These processes include:

• Group working using a range of techniques (e.g. leadership; interaction with other students, supervisors, research fellows)
• Building confidence from completion of assignments, and via projects, presentations and essays.
• Collaborate efficiently and productively with others in the process of learning and presenting conclusions
• Organise their own learning, manage workload and work to a timetable
• Effectively plan, and possess the confidence to undertake and to present scholarly work that demonstrates an understanding of the aims, methods and considerations, and ability to form their own conclusions
• Work independently on the creation of essays and reports.
• Learning study techniques such as literature reading.
• Learning to analyse individual strengths and weaknesses through provided written and oral feedback.

Programme outcomes: Technical/practical skills

Practical skills, which are all computer based, are acquired mainly through problem solving exercises and the research project. Quantitative and statistical skills are included within all course options.

• Ability to effectively design programmes
• Ability to design and contribute to the development of statistical databases
• Enhanced scientific communication skills
• Ability to analyse high-throughput data
• Acquire skills working in on-line environments
• Ability of maintain and establish complex computer systems

Programme structure and features

Modes of Study:

The programme is offered on a 1 Year full time basis. A variety of teaching methods are used including lectures, tutorials, computer-based and experimental practicals. The final stage of the course is a research project undertaken with a research group. The taught programme runs from mid September to the end of May, and the research project taking place from May to mid-August.

Progression and Exit Awards:

Students who gain >=50% overall and >=50% in at least 80 of the 120 credits in the final overall assessment of the taught stage at the end of May can proceed to the dissertation stage, and carry out a full-time research project from June – August.

Students who gain >=40% overall and >=40% in at least 80 of the 120 credits in the final overall assessment who do not qualify to proceed will be awarded the Diploma and leave in June.  Note than one 20 credit course is marked Pass or Fail, a pass in this course counts as 20 credits >=50%. Students are not allowed to take more than 30 credits below Level 11.

To be awarded the MSc, students must successfully complete both the taught and dissertation stages.  Students may elect to exit at the end of the taught stage with the award of Diploma. Both the MSc and the Diploma may be awarded with Distinction.

Students achieving an average >=70% in the taught component, as well as achieving >=70% for their dissertation may be eligible for an MSc with distinction.

Curriculum:

The taught component of the programme will comprise 120 credit points made up of recommended and optional courses. This will be followed by a full time dissertation project over the summer, worth a total of 60 credit points.

A full breakdown of the course options available with credit points and levels is given below. Courses in bold are compulsory, but occasionally students may select appropriate courses outside this selection. Courses in italic are recommended, depending upon the students’ individual training needs.

Taught Stage (120 credits)

Semester One

Semester Two

Dissertation Stage (60 credits)

Assessment:

Typically assessment is by examination or by written assignment.  Other methods used include assessed data analysis and programming tasks and individual and group presentations.

Teaching and learning methods and strategies

Teaching and Learning strategies employed at the University of Edinburgh consist of a variety of different methods appropriate to the programme aims.  The graduate attributes listed above are met through a teaching and learning framework (detailed below) which is appropriate to the level and content of the programme.

Teaching and Learning Activities include:

Lectures

Computer based practicals

Workshops

Oral Presentations

Poster Presentations

Laboratories

Tutorials

Seminars

Discussion Groups/Project Groups

Problem based learning activities

Examples: Students attend problem based tutorial sessions, one to one meetings with programme director, project work in a research laboratory; students carry out their own research at the frontier of knowledge and can make a genuine contribution to the progress of original research.  This also involves reviewing relevant papers, analysing data, writing a report and giving a presentation.

Flexible Learning Week

The University of Edinburgh Flexible Learning Week is scheduled in Week 6 of Semester 2. During this week ‘normal’ teaching is suspended which provides space outwith the curriculum for staff and students to explore new learning activities. Bioinformatics students are encouraged to take part in a number of types of activities held in Biological Sciences were workshops, peer assisted learning activities, public engagement activities, careers events and field trips.

Assessment methods and strategies

Courses are assessed by a diverse range of methods and it often takes the form of formative work which provides the student with on-going feedback as well as summative assessment which is submitted for credit.

Essays; students are provided with written feedback

Assessed Problems; students are provided with written feedback

Oral Presentations; feedback is provided by peers and staff

On-line Tests; on-line feedback with explanations

Written Degree Examinations; students have the opportunity to meet with course organisers to view their examination scripts.

Multiple Choice Tests

Project Reports and Presentations; students are provided with written feedback.

Career opportunities

Bioinformatics graduates have a wide range of career options both commercial and academic settings.

Bioinformatics graduate employers can range from the NHS, universities, service laboratories and a wide variety of life science companies.

Equally this qualification is great preparation for further research at PhD level and a number of our current students plan to continue their studies after graduation.

Other items

The MSc in Bioinformatics is one of several taught Masters programmes offered by the School of Biological Sciences at the University of Edinburgh.

The Programme Director of the MSc in Bioinformatics is responsible for academic and pastoral guidance for students on the course.  Throughout a student’s time at the university the Programme Director guides the student in choice of courses and provides general support.

Courses are administered and run through Teaching Organisations. These produce detailed course guides for new students. These guides provide details of courses and also advise students on assessment and general university policy and regulations.

The Degree Programme Tables (DPT) for the Bioinformatics programme can be found at:

http://www.drps.ed.ac.uk/index.php