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MSc Bioinformatics and Computational Genomics

Academic Year 2021/22

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

HECoS Code

100270

ATAS Clearance Required

Health Check Required

Portfolio Required

Interview Required

Mode of Study

Full Time

Type of Programme

Postgraduate

Length of Programme

Full Time - 1 Academic Year(s)

Total Credits for Programme

180

Exit Awards available

INSTITUTE INFORMATION

Teaching Institution	Queen's University Belfast
School/Department	Medicine, Dentistry and Biomedical Sciences
Quality Code https://www.qaa.ac.uk/quality-code Higher Education Credit Framework for England https://www.qaa.ac.uk/quality-code/higher-education-credit-framework-for-england	Level 7
Subject Benchmark Statements https://www.qaa.ac.uk/quality-code/subject-benchmark-statements The Frameworks for Higher Education Qualifications of UK Degree-Awarding Bodies https://www.qaa.ac.uk/docs/qaa/quality-code/qualifications-frameworks.pdf	N/A
Accreditations (PSRB)

REGULATION INFORMATION

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

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) Students must pass all taught modules and the dissertation to be awarded the degree of Master of Science in Bioinformatics and Computational Genomics.

2) Any student who fails 60 credits or more of taught modules will not be able to proceed to the dissertation module until the taught modules are successfully completed. Students can then choose whether to graduate with the Postgraduate Diploma or re-enrol to take the Dissertation in the next academic year

3) In the case of failed modules, students will normally be permitted one resit attempt in each module. Students who fail any module twice will normally be required to withdraw.

4) Students 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.

5) Students who have failed to pass all taught modules but who have accumulated a minimum of 60 CATS points will be eligible for the award of Postgraduate Certificate in Bioinformatics & Computational Genomics

Students with protected characteristics

Are students subject to Fitness to Practise Regulations

(Please see General Regulations)

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

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.

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 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:

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 omics 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

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.

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

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

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

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.

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

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 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

Learning Outcomes: Knowledge & Understanding

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

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

Methods of Assessment

Coursework assignments

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		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%
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		30%	70%	0%
Biostatistical Informatics	SCM8109	7	20		YES	12 weeks	N	YES		100%	0%	0%
Health and Biomedical informatics and the exposome	SCM8148	7	10		YES	6 weeks	N	YES		80%	20%	0%
Systems Medicine: from Molecules to Populations	SCM8152	7	10		YES	6 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.

detail

MSc Bioinformatics and Computational Genomics

Academic Year 2021/22

INSTITUTE INFORMATION

REGULATION INFORMATION

EDUCATIONAL AIMS OF PROGRAMME

LEARNING OUTCOMES

Learning Outcomes: Cognitive Skills

Learning Outcomes: Transferable Skills

Learning Outcomes: Knowledge & Understanding

Learning Outcomes: Subject Specific

Learning Outcomes: Knowledge & Understanding

MODULE INFORMATION

Notes