detail

  • detail

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
(exit route if applicable for Postgraduate Taught Programmes)

Postgraduate Diploma

Programme Code

MED-PD-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

Candidates cannot apply for entry to the Postgraduate Diploma in Bioinformatics and Computational Genomics; it is only available to those who have successfully completed the taught components of the Master of Science in Bioinformatics and Computational Genomics, but do not complete or pass the Dissertation.

Additional Relevant Information:
For further Information Refer to:
School of Medicine, Dentistry and Biomedical Sciences
Postgraduate and Professional Development
Whitla Medical Building97 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

120

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)

External Examiner Name:

External Examiner Institution/Organisation

Professor Jean Baptiste Caizer

University of Birmingham

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 Postgraduate Diploma in Bioinformatics and Computational Genomics is offered as 1 year full-time course.

2) Candidates must pass all taught modules (120 CATS points) to be awarded the Postgraduate Diploma 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) 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

None

Are students subject to Fitness to Practise Regulations

(Please see General Regulations)

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

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 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 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 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 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 & 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.

Elucidate 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.

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.
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

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.
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 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

 

Assessment

 

 

 

 

S1

S2

 

 

Core

Option

Coursework %

Practical %

Examination %

Scientific Programming & Statistical Computing

SCM7047

70

20

YES

12 weeks

N

YES

100%

0%

0%

Analysis of Gene Expression

SCM8051

70

20

YES

12 weeks

N

YES

80%

20%

0%

Genomics and Human Disease

SCM8095

70

20

YES

10 weeks

N

YES

70%

30%

0%

Applied Genomics

SCM8108

70

20

YES

12 weeks

N

YES

100%

0%

0%

Biostatistical Informatics

SCM8109

70

20

YES

12 weeks

N

YES

100%

0%

0%

Digital Pathology Distance Learning

SCM8124

70

20

YES

12 weeks

N

YES

100%

0%

0%

Notes

in 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.