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MSc Bioinformatics and Computational Genomics

Academic Year 2016/17

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 Bioinformatics and Computational Genomics

Final Award
(exit route if applicable for Postgraduate Taught Programmes)

Master of Science

Programme Code

MED-MSC-BC

UCAS Code

JACS Code

A900 (DESCR) 100

Criteria for Admissions

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 within QUB 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.

To have an intercalated application considered, an external candidate must be ranked in the top half of their year cohort. A student must normally have passed all assessments at first attempt for the year in which they are applying


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
Whitla Medical Building 97 Lisburn Road
Belfast BT9 7BL
www.qub.ac.uk/schools/mdbs/
Tel: +44 (0) 28 9097 2615
Email: pgoffice.smdb@qub.ac.uk

ATAS Clearance Required

No

Health Check Required

No

Portfolio Required

Interview Required

Mode of Study

Full Time

Type of Programme

Postgraduate

Length of Programme

1 Academic Year(s)

Total Credits for Programme

180

Exit Awards available

INSTITUTE INFORMATION

Awarding Institution/Body

Queen's University Belfast

Teaching Institution

Queen's University Belfast

School/Department

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

N/A

Accreditations (PSRB)

Date of most recent Accreditation Visit

External Examiner Name:

External Examiner Institution/Organisation

Professor Mark Girolami

University College London

REGULATION INFORMATION

Does the Programme have any approved exemptions from the University General Regulations
(Please see General Regulations)

No

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.

5) A candidate who fails the dissertation may re-submit the dissertation within 6 months. Normally only one 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

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

Are students subject to Fitness to Practise Regulations

(Please see General Regulations)

No

EDUCATIONAL AIMS OF PROGRAMME

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:

Gain an in-depth understanding of genomics as well as with state-of-the-art computational and statistical methodologies related to genomics research.

Evaluate current and future developments in Bioinformatics and Computational Genomics.

Participate in original research.

Develop skills in scientific writing.

Build knowledge and research skills for progression to PhD programmes.

Develop an understanding of their professional and ethical responsibilities and of the impact of biomolecular informatics and biotechnology in society

Undertake a substantial piece of research in Bioinformatics and Computational

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.

LEARNING OUTCOMES

Learning Outcomes: Cognitive Skills

On the completion of this course successful students will be able to:

Critically evaluate scientific literature.

Teaching/Learning Methods and Strategies

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

Methods of Assessment

Coursework assignments
Dissertation

Describe how to manage and interrogate complex systems

Teaching/Learning Methods and Strategies

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

Methods of Assessment

Coursework assignments
Dissertation

Efficiently analyse and summarise core concepts from diverse sources.

Teaching/Learning Methods and Strategies

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

Methods of Assessment

Coursework assignments
Dissertation

Creatively apply and extend scientific principles to new problems.

Teaching/Learning Methods and Strategies

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

Methods of Assessment

Coursework assignments
Dissertation

Learning Outcomes: Transferable Skills

On the completion of this course successful students will be able to:

On successful completion of this programme students will have gained or increased competence in:
Critical, analytical and creative thinking.

Teaching/Learning Methods and Strategies

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

Methods of Assessment

Coursework, oral presentations, Dissertation

Oral communication and in writing scientific documentation

Teaching/Learning Methods and Strategies

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

Methods of Assessment

Coursework, oral presentations, Dissertation

Handling various types of IT resources.

Teaching/Learning Methods and Strategies

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

Methods of Assessment

Coursework, oral presentations, Dissertation

Time management

Teaching/Learning Methods and Strategies

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

Methods of Assessment

Coursework, oral presentations, Dissertation

Team work

Teaching/Learning Methods and Strategies

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

Methods of Assessment

Coursework, oral presentations, Dissertation

Learning Outcomes: Knowledge & Understanding

On the completion of this course successful students will be able to:

Explain how genetics and genomics contribute to medicine and science.

Teaching/Learning Methods and Strategies

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.

Methods of Assessment

Coursework assignments, oral presentations and practical assignments

Communicate the principles of cell biology.

Teaching/Learning Methods and Strategies

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.

Methods of Assessment

Coursework assignments, oral presentations and practical assignments

Perform statistical analyses and interpret the output from such analyses

Teaching/Learning Methods and Strategies

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.

Methods of Assessment

Coursework assignments, oral presentations and practical assignments

Explain basic principles of statistical and machine learning methods.

Teaching/Learning Methods and Strategies

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.

Methods of Assessment

Coursework assignments, oral presentations and practical assignments

Utilise the basic elements of programming languages such as R

Teaching/Learning Methods and Strategies

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

Methods of Assessment

Coursework assignments, oral presentations and practical assignments

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

Teaching/Learning Methods and Strategies

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.

Methods of Assessment

Coursework assignments, oral presentations and practical assignments

Appraise the theoretical and technical aspects of digital pathology and have an appreciation for the regulatory requirements relating to digital pathology for research and clinical application

Teaching/Learning Methods and Strategies

On line lectures, online tutorials and guided self-directed learning

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

Methods of Assessment

Coursework, Assignments

Communicate the importance of data integration and methods to deal with complex systems and associated data

Teaching/Learning Methods and Strategies

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

Methods of Assessment

Coursework assignments, oral presentations and practical assignments

Learning Outcomes: Subject Specific

On the completion of this course successful students will be able to:

Select, apply and interpret statistical methods in the analysis of medical data

Teaching/Learning Methods and Strategies

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

Methods of Assessment

Coursework, oral presentations, and Dissertation

Interrogate relevant online resources for efficient data retrieval and analysis

Teaching/Learning Methods and Strategies

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

Methods of Assessment

Coursework, oral presentations, and Dissertation

Utilise comprehensive programming skills.

Teaching/Learning Methods and Strategies

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

Methods of Assessment

Coursework, oral presentations, and Dissertation

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

Teaching/Learning Methods and Strategies

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

Methods of Assessment

Coursework, oral presentations, and Dissertation

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

Teaching/Learning Methods and Strategies

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

Methods of Assessment

Coursework, oral presentations, and Dissertation

Discuss digital pathology platforms and evaluate how different image analyse approaches are used for research and clinical application

Teaching/Learning Methods and Strategies

On line lectures, online tutorials and guided self-directed learning

Methods of Assessment

Coursework

MODULE INFORMATION

Programme Requirements

Module Title

Module Code

Level/ stage

Credits

Availability

Duration

Pre-requisite

 

Assessment

 

 

 

 

S1

S2

 

 

Core

Option

Coursework %

Practical %

Examination %

Scientific Programming & Statistical Computing

SCM7047

7

20

YES

12 weeks

N

YES

100%

0%

0%

Analysis of Gene Expression

SCM8051

7

20

YES

12 weeks

N

YES

80%

20%

0%

Dissertation

SCM8053

7

60

36 weeks

N

YES

100%

0%

0%

Genomics and Human Disease

SCM8095

7

20

YES

10 weeks

N

YES

70%

30%

0%

Applied Genomics

SCM8108

7

20

YES

12 weeks

N

YES

100%

0%

0%

Biostatistical Informatics

SCM8109

7

20

YES

12 weeks

N

YES

100%

0%

0%

Digital Pathology Distance Learning

SCM8124

7

20

YES

12 weeks

N

YES

100%

0%

0%

Notes

In addition to the modules above students will also have an Introductory module (SCM7046 Introductory Cell Biology and Computational Analysis) which is attendance only and runs at the start of Semester 1.