Diploma in Bioinformatics and Computational Genomics
Academic Year 2017/18
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 |
Diploma in Bioinformatics and Computational Genomics |
Final Award |
Postgraduate Diploma |
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Programme Code |
MED-PD-BC |
UCAS Code |
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JACS Code |
A900 (DESCR) 100 |
Criteria for Admissions For current general University entry requirements for this pathway go to: |
ATAS Clearance Required |
No |
Health Check Required |
No |
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Portfolio Required |
Interview Required |
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Mode of Study |
Full Time |
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Type of Programme |
Postgraduate |
Length of Programme |
1 Academic Year(s) |
Total Credits for Programme |
120 |
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Exit Awards available |
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INSTITUTE INFORMATION
Awarding Institution/Body |
Queen's University Belfast |
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Teaching Institution |
Queen's University Belfast |
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School/Department |
Medicine, Dentistry and Biomedical Sciences |
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Framework for Higher Education Qualification Level |
Level 7 |
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QAA Benchmark Group |
N/A |
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Accreditations (PSRB) |
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 None |
Are students subject to Fitness to Practise Regulations (Please see General Regulations) No |
EDUCATIONAL AIMS OF PROGRAMME
The overall aim of the Postgraduate Diploma in Bioinformatics and Computational Genomics is to offer a high quality supportive teaching and learning environment that gives students the opportunity to:
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.
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.
Develop an understanding of their professional and ethical responsibilities and of the impact of biomolecular informatics and biotechnology in society.
LEARNING OUTCOMES
Learning Outcomes: Cognitive SkillsOn the completion of this course successful students will be able to: |
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Critically evaluate scientific literature. |
Teaching/Learning Methods and Strategies Tutorial-based discussion, self-directed study and practical exercises Methods of Assessment Coursework assignments. |
Describe how to manage and interrogate and understand complex systems |
Teaching/Learning Methods and Strategies Tutorial-based discussion, self-directed study and practical exercises 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 and practical exercises 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 and practical exercises Methods of Assessment Coursework assignments. |
Learning Outcomes: Transferable SkillsOn the completion of this course successful students will be able to: |
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On successful completion of this programme students will have gained or increased competence in: |
Teaching/Learning Methods and Strategies Tutorial-based discussion, practical exercises and coursework assignments Methods of Assessment Coursework and oral presentations. |
Oral communication and in writing scientific documentations. |
Teaching/Learning Methods and Strategies Tutorial-based discussion, practical exercises and coursework assignments Methods of Assessment Coursework and oral presentations. |
Handling various types of IT resources. |
Teaching/Learning Methods and Strategies Tutorial-based discussion, practical exercises and coursework assignments Methods of Assessment Coursework and oral presentations. |
Time management. |
Teaching/Learning Methods and Strategies Tutorial-based discussion, practical exercises and coursework assignments Methods of Assessment Coursework and oral presentations. |
Team work. |
Teaching/Learning Methods and Strategies Tutorial-based discussion, practical exercises and coursework assignments Methods of Assessment Coursework and oral presentations. |
Learning Outcomes: Knowledge & UnderstandingOn the completion of this course successful students will be able to: |
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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. Methods of Assessment Coursework assignments, oral presentations and practical assignments. |
Elucidate 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 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 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. Methods of Assessment Coursework assignments, oral presentations and practical assignments. |
Apply and explain 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. Methods of Assessment Coursework assignments |
Analyse the computational complexity of structure prediction problems. |
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. |
Learning Outcomes: Subject SpecificOn the completion of this course successful students will be able to: |
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Select, apply and interpret statistical methods in the analysis of medical data. |
Teaching/Learning Methods and Strategies Tutorials, practical exercises, coursework assignments and oral presentations Methods of Assessment Coursework and oral presentations |
Interrogate relevant online resources for efficient data retrieval and analysis. |
Teaching/Learning Methods and Strategies Tutorials, practical exercises, coursework assignments and oral presentations Methods of Assessment Coursework and oral presentations |
Utilise comprehensive programming skills. |
Teaching/Learning Methods and Strategies Tutorials, practical exercises, coursework assignments and oral presentations Methods of Assessment Coursework and oral presentations |
Formulate and devise new algorithmic solutions for problems arising from biomedical research. |
Teaching/Learning Methods and Strategies Tutorials, practical exercises, coursework assignments and oral presentations Methods of Assessment Coursework and oral presentations |
Utilise a variety of existing databases and structure prediction tools in biomedical research. |
Teaching/Learning Methods and Strategies Tutorials, practical exercises, coursework assignments and oral presentations Methods of Assessment Coursework and oral presentations |
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 |
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Assessment |
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S1 |
S2 |
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Core |
Option |
Coursework % |
Practical % |
Examination % |
Scientific Programming & Statistical Computing |
SCM7047 |
70 |
20 |
YES |
12 weeks |
N |
YES |
100% |
0% |
0% |
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Analysis of Gene Expression |
SCM8051 |
70 |
20 |
YES |
12 weeks |
N |
YES |
80% |
20% |
0% |
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Genomics and Human Disease |
SCM8095 |
70 |
20 |
YES |
10 weeks |
N |
YES |
70% |
30% |
0% |
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Applied Genomics |
SCM8108 |
70 |
20 |
YES |
12 weeks |
N |
YES |
100% |
0% |
0% |
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Biostatistical Informatics |
SCM8109 |
70 |
20 |
YES |
12 weeks |
N |
YES |
100% |
0% |
0% |
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Digital Pathology Distance Learning |
SCM8124 |
70 |
20 |
YES |
12 weeks |
N |
YES |
100% |
0% |
0% |
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Notesin addition to the modules listed students will also have an introductory module SCM7046 Introductory Cell Biology and Computational Analysis which is attendance only at the start of semester one. |