Programme Specification ( ): MSc in Bioinformatics and Computational Genomics

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Date of Revision Date of Previous Revision

Programme Specification (2014-15): MSc in Bioinformatics and Computational

Genomics

A programme specification is required for any programme on which a student may be registered. All programmes of the University are subject to the University’s Quality Assurance and Enhancement processes as set out in the DASA Policies and Procedures Manual.

Programme Title MSc in Bioinformatics and Computational Genomics

Final Award

(exit route if applicable for PGT Programmes)

Master of Science (Exit route: PG Diploma)

Programme Code MED-MSC-BC UCAS Code NA JACS Code A9900

Criteria for Admissions (Please see General Regulations)

For current general University entry requirements for this pathway go to: http://www.qub.ac.uk/ado

 Successful completion of an undergraduate degree programme with a grade average of at least 60% or a 2:1 honours degree (or equivalent) in a Natural Science subject, Mathematics, Computer Science, or a relevant medical subject (e.g., Genetics, Molecular Biology, Biomedical Sciences).

 Intercalating medical and dental students will also be considered if they have successfully completed the third year of their course and achieved at least an upper second class honours standard. Applicants may be required to undertake an interview. Intercalating applicants should also ensure they have permission to intercalated from either the Director for Medical Education or Dentistry as appropriate.

 International applicants should have either:

- an IELTS score of 6.5 with not less than 6.0 in each of the four component elements of listening, reading, speaking and writing taken within the last 2 years;

- a TOEFL score of 90+ (internet based test), taken within the last 2 years, with minimum component scores of Listening – 20, Reading – 19, Speaking – 21 and Writing – 20;

- a valid Certificate of Proficiency in English grade A or B; - a valid Certificate of Advanced English grade A; or

- a first or upper second class honours degree from a university based in the UK, Republic of Ireland or other suitably quality assured location where the medium of instruction is English.

Additional Relevant Information: For further Information Refer to:

School of Medicine, Dentistry and Biomedical Sciences Postgraduate and Professional Development

Health Sciences Building 97 Lisburn Road

Belfast BT9 7BL

www.qub.ac.uk/schools/mdbs/ Tel: +44 (0) 28 9097 2615

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Mode of Study (Full-time, Part-time, other) Full-time

Type of Programme

Master of Science Length of Programme

1 Year Total Credits for Programme

180

Awarding Institution/Body Queen’s University Belfast

Teaching Institution _{Queen’s University Belfast}

School/Department School of Medicine, Dentistry and Biomedical Sciences

Framework for Higher Education Qualification Level

http://www.qaa.ac.uk/publications/information-and-guidance

Level 7

QAA Benchmark Group

http://www.qaa.ac.uk/assuring-standards-and- quality/the-quality-code/subject-benchmark-statements

NA

Collaborative Organisation and form of Collaboration (if applicable)

NA

Accreditations (PSRB)

None Date of next scheduled accreditation visit

NA

ATAS Clearance NA

External Examiner Name: External Examiner Institution/Organisation:

Professor Mark Girolami University College London

Does the Programme have any approved exemptions from the University General Regulations

(Please see General Regulations)

No

(If yes, please state here any exemptions to regulations which have been approved for this programme)

Programme Specific Regulations

AWARDS, CREDITS AND PROGRESSION OF LEARNING OUTCOMES

The following regulations should be read in conjunction with the General Regulations of the University. 1) The Master of Science in Bioinformatics and Computational Genomics is offered as 1 year full-time course. 2) Candidates must pass all taught modules and the dissertation to be awarded the degree of Master of Science in Bioinformatics and Computational Genomics.

3) The maximum mark which a repeat module can contribute to the award will be 50% although the actual mark achieved will be recorded on the transcript.

4) Candidates will be asked to submit a dissertation of 15,000- 20,000 words by 15th of September.

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resubmission will be permitted.

6) Candidates who pass all the taught modules but who fail to achieve a mark of at least 50% in the dissertation shall be eligible for the award of Postgraduate Diploma in Bioinformatics and Computational Genomics. 7) Candidates who pass all the taught modules but who fail to submit a dissertation, or fail the dissertation following resubmission, shall be eligible for the award of Postgraduate Diploma in Bioinformatics and Computational Genomics.

8) All decisions on progress will be made by the Board of Examiners.

Examinations

All taught modules will be assessed through coursework which may include oral presentations and practical assignments. A pass mark of 50% is mandatory in all modules in accordance with the general regulations of the University.

Students with protected characteristics NA

Are students subject to Fitness to Practise Regulations

(Please see General Regulations)

Please indicate No (with the exception of students who are taking this as an intercalated degree and whose primary programmes are subject to FTP regulations) Fitness to Practise programmes are those which permit students to enter a profession which is itself subject to Fitness to Practise rules

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Educational Aims of Programme On completion of the programme the student will be able to:

The overall aim of the Master of Science in Bioinformatics and Computational Genomics is to offer a high quality supportive teaching and learning environment that gives students the opportunity to:

1) Develop systematic knowledge and experience in theoretical foundations and

practical skills in computational science, statistical analysis, programming and data interpretation for modern molecular biology and genomics.

2) Gain an in-depth understanding of genomics as well as with state-of-the-art computational and statistical methodologies related to genomics research. 3) Evaluate current and future developments in Bioinformatics and Computational Genomics.

4) Participate in original research. 5) Develop skills in scientific writing.

6) Build knowledge and research skills for progression to PhD programmes.

7) Develop an understanding of their professional and ethical responsibilities and of the impact of biomolecular informatics and biotechnology in society. 8) Undertake a substantial piece of research in Bioinformatics and Computational Genomics

Learning Outcomes: Cognitive Skills

On the completion of this course successful students will be able to: Teaching/Learning Methods and Strategies

Methods of Assessment

Critically evaluate scientific literature.

Tutorial-based discussion, self-directed study, practical exercises, and through work on the MSc thesis.

Coursework assignments Dissertation

Describe how to manage and interrogate complex systems Tutorial-based discussion, self-directed study, practical exercises, and through work on the MSc thesis.

Coursework assignments

Dissertation Efficiently analyse and summarise core concepts from diverse sources. Tutorial-based discussion, self-directed

study, practical exercises, and through work on the MSc thesis.

Dissertation Creatively apply and extend scientific principles to new problems. Tutorial-based discussion, self-directed

study, practical exercises, and through work

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on the MSc thesis.

Learning Outcomes: Transferable Skills

On the completion of this course successful students will be able to: Teaching/Learning Methods and Strategies Methods of Assessment

On successful completion of this programme students will have gained or increased competence in:

Critical, analytical and creative thinking.

Tutorial-based discussion, practical exercises, coursework assignments, through work on the MSc thesis.

Coursework, oral presentations, Dissertation

Oral communication and in writing scientific documentations. Tutorial-based discussion, practical exercises, coursework assignments, through work on the MSc thesis.

Handling various types of IT resources. Tutorial-based discussion, practical exercises, coursework assignments, through work on the MSc thesis.

Time management. Tutorial-based discussion, practical exercises,

coursework assignments, through work on the MSc thesis.

Team work. Tutorial-based discussion, practical exercises,

coursework assignments, through work on the MSc thesis.

Learning Outcomes: Knowledge and Understanding

On the completion of this course successful students will be able to: Teaching/Learning Methods and Strategies Methods of Assessment

Explain how genetics and genomics contribute to medicine and science.

Lectures and tutorials. Self-directed learning is strongly represented in all modules.

Practical teaching is used in most of the modules. Study material will be largely derived from core text books and also from journal articles.

Coursework assignments, oral

presentations and practical assignments.

Communicate the principles of cell biology. Lectures and tutorials. Self-directed learning is strongly represented in all modules.

Practical teaching is used in most of the modules. Study material will be largely derived

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from core text books and also from journal articles.

Perform statistical analyses and interpret the output from such analyses.

presentations and practical assignments

Explain basic principles of statistical and machine learning methods. Lectures and tutorials. Self-directed learning is strongly represented in all modules.

Utilise the basic elements of programming languages such as R. Lectures and tutorials. Self-directed learning is strongly represented in all modules.

Elucidate the practical steps involved in performing a microarray, massively parallel sequencing or proteomic profiling analysis.

Develop computational solutions for image interpretation and analysis Lectures and tutorials. Self-directed learning is strongly represented in all modules.

Practical teaching is used in most of the modules. Study material will be largely derived from core text books and also from journal

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articles. Communicate the importance of data integration and methods to deal

with complex systems and associated data

Develop solutions for the quantitative and statistical analysis of medical images

Practical teaching is used in most of the modules. Study material will be largely derived from in house course materials and also from journal articles

Learning Outcomes: Subject Specific Skills

On the completion of this course successful students will be able to Teaching/Learning Methods and Strategies Methods of Assessment

Select, apply and interpret statistical methods in the analysis of

medical data. Tutorials, practical exercises, coursework

assignments, oral presentations, and through work on the MSc thesis.

Coursework, oral presentations, and Dissertation

Interrogate relevant online resources for efficient data retrieval and

analysis. Tutorials, practical exercises, coursework

assignments, oral presentations, and through work on the MSc thesis.

Utilise comprehensive programming skills. Tutorials, practical exercises, coursework assignments, oral presentations, and through work on the MSc thesis.

Formulate and devise new algorithmic solutions for problems arising from biomedical research.

Tutorials, practical exercises, coursework assignments, oral presentations, and through work on the MSc thesis.

Utilise a variety of existing databases and structure prediction tools in biomedical research.

Tutorials, practical exercises, coursework assignments, oral presentations, and through work on the MSc thesis.

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Programme Requirements

Module Title Module Code

Level/ stage

Credits Availability Duration Pre-requisite Assessment

S1 S2 Core Option Coursework % Examination %

Introductory Cell Biology and Computational Analysis

SCM7046 0 √ 2 weeks None  No Assessment

Scientific Programming & Statistical Computing

SCM7047 20 √ 12 weeks None  Coursework -

100%

Genomics and Genetics SCM7048 20 √ 12 weeks None  Oral

presentation- 40% Written Assignment - 60% Analysis of Gene Expression

SCM8051 20 √ 12 weeks None  Essay - 80%

Presentation - 20%

Computational Diagnostics SCM8049 20 √ 12 weeks None  Coursework (2) -

100%

Statistical Learning and Genomics

SCM8050 20 √ 12 weeks None  Coursework (2) -

100%

Bioimaging Informatics SCM7049 20 √ 12 weeks None  Coursework -

100%

Dissertation SCM8053 60 √ √ Full Year None  Dissertation -

100%

Approved by Director of Education: