Postgraduate Programme Specification
MSc Bioinformatics and Computational Genomics
Academic Year 2022/23
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 processes. All degrees are awarded by Queen's University Belfast.
| 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 | HECoS Code |
100270 - Medical sciences - 60 100869 - Bioinformatics - 20 100901 - Genomics - 20 |
||||||||||
ATAS Clearance Required |
No |
Health Check Required |
No |
|||||||||||
Portfolio Required |
-- |
Interview Required |
-- |
|||||||||||
| Mode of Study | Full Time | |||||||||||||
| Type of Programme | Postgraduate | Length of Programme |
Full Time - 1 Academic Year |
Total Credits for Programme | 180 | |||||||||
| Exit Awards available | No | |||||||||||||
Institute Information
Teaching Institution |
Queen's University Belfast |
School/Department |
Medicine, Dentistry and Biomedical Sciences |
|
Quality Code Higher Education Credit Framework for England |
Level 7 |
|
Subject Benchmark Statements The Frameworks for Higher Education Qualifications of UK Degree-Awarding Bodies |
N/A |
Accreditations (PSRB) |
|
No accreditations (PSRB) found. | |
Regulation Information
|
Does the Programme have any approved exemptions from the University General Regulations No |
|
Programme Specific Regulations AWARDS, CREDITS AND PROGRESSION OF LEARNING OUTCOMES |
|
Students with protected characteristics NA |
|
Are students subject to Fitness to Practise Regulations (Please see General Regulations) Yes |
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 bioinformatics and biotechnology in society
Undertake a substantial piece of research in Bioinformatics and Computational Genomics
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 SkillsOn 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 |
|
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 |
|
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 |
|
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 |
Learning Outcomes: Transferable SkillsOn the completion of this course successful students will be able to: |
|
|
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 & UnderstandingOn the completion of this course successful students will be able to: |
|
|
Explain how genetics and omics contribute to medicine and science. |
Teaching/Learning Methods and Strategies Lectures and tutorials. Self-directed learning is strongly represented in all modules. 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. 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, including online and tutorials. Self-directed learning is strongly represented in all modules. 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. 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. Methods of Assessment Coursework assignments, oral presentations and practical assignments |
|
Elucidate the practical steps involved in performing a range of omics analysis. |
Teaching/Learning Methods and Strategies Lectures and tutorials. Self-directed learning is strongly represented in all modules. Methods of Assessment Coursework assignments, oral presentations and practical assignments |
|
Appraise the theoretical and technical aspects of systems medicine and have an appreciation of its application research and clinical activity |
Teaching/Learning Methods and Strategies Lectures and tutorials. Self-directed learning is a feature of all modules. 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 a feature of all modules. Methods of Assessment Coursework assignments, oral presentations and practical assignments |
|
Explain the key concepts in health informatics and its integration with translational bioinformatics to facilitate the development of precision medicine approaches |
Teaching/Learning Methods and Strategies Lectures and tutorials. Self-directed learning is strongly represented in all modules. Methods of Assessment Coursework assignments |
Learning Outcomes: Subject SpecificOn the completion of this course successful students will be able to: |
|
|
Select, apply and interpret statistical methods in the analysis of biomedical, omics and clinical 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 |
Module Information
Stages and Modules
| Module Title | Module Code | Level/ stage | Credits | Availability |
Duration | Pre-requisite | Assessment |
|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| S1 | S2 | Core | Option | Coursework % | Practical % | Examination % | ||||||
| Systems Medicine: from Molecules to Populations | SCM8152 | 7 | 10 | -- | YES | 6 weeks | N | YES | -- | 100% | 0% | 0% |
| Biostatistical Informatics | SCM8109 | 7 | 20 | -- | YES | 12 weeks | N | YES | -- | 100% | 0% | 0% |
| Genomics and Human Disease | SCM8095 | 7 | 20 | YES | -- | 10 weeks | N | YES | -- | 70% | 30% | 0% |
| Health and Biomedical informatics and the exposome | SCM8148 | 7 | 10 | -- | YES | 6 weeks | N | YES | -- | 80% | 20% | 0% |
| Applied Genomics | SCM8108 | 7 | 20 | -- | YES | 12 weeks | N | YES | -- | 30% | 70% | 0% |
| Scientific Programming & Statistical Computing | SCM7047 | 7 | 20 | YES | -- | 10 weeks | N | YES | -- | 100% | 0% | 0% |
| Analysis of Gene Expression | SCM8051 | 7 | 20 | YES | -- | 10 weeks | N | YES | -- | 75% | 25% | 0% |
| Dissertation | SCM8053 | 7 | 60 | -- | YES | 16 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.