Programme Specification ( ): MSc in Bioinformatics and Computational Genomics

Transcription

1 Date of Revision Date of Previous Revision Programme Specification ( ): MSc in Bioinformatics and Computational Genomics 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 in Bioinformatics and Computational Genomics Final Award (exit route if applicable for PGT Programmes) Programme Code MED-MSC-BC UCAS Code JACS Code A9900 Criteria for Admissions (Please see General Regulations) Master of Science (Exit route: PG Diploma) For current general University entry requirements for this pathway go to: 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 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. 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 Health Sciences Building 97 Lisburn Road Belfast BT9 7BL Tel: +44 (0)

2 Mode of Study (Full-time, Part-time, other) Full-time Type of Programme Master of Science Length of Programme 1 Year Total Credits for Programme 180 Awarding Institution/Body Teaching Institution School/Department Framework for Higher Education Qualification Level Queen s University Belfast Queen s University Belfast School of Medicine, Dentistry and Biomedical Sciences Level 7 QAA Benchmark Group Collaborative Organisation and form of Collaboration (if applicable) Accreditations (PSRB) ATAS Clearance None External Examiner Name: Professor Mark Girolami Date of next scheduled accreditation visit External Examiner Institution/Organisation: University College London Does the Programme have any approved exemptions from the University General Regulations (Please see General Regulations) No (If yes, please state here any exemptions to regulations which have been approved for this programme) 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

3 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 Are students subject to Fitness to Practise Regulations (Please see General Regulations) Length of Programme 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 1 Year

4 Educational Aims of Programme On completion of the programme the student will be able to: 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: 1) 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. 2) Gain an in-depth understanding of genomics as well as with state-of-the-art computational and statistical methodologies related to genomics research. 3) Evaluate current and future developments in Bioinformatics and Computational Genomics. 4) Participate in original research. 5) Develop skills in scientific writing. 6) Build knowledge and research skills for progression to PhD programmes. 7) Develop an understanding of their professional and ethical responsibilities and of the impact of biomolecular informatics and biotechnology in society. 8) Undertake a substantial piece of research in Bioinformatics and Computational Genomics Learning Outcomes: Cognitive Skills On the completion of this course successful students will be able to: Critically evaluate scientific literature. Describe how to manage and interrogate complex systems Efficiently analyse and summarise core concepts from diverse sources. 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 Tutorial-based discussion, self-directed study, practical exercises, and through work on the Tutorial-based discussion, self-directed study, practical exercises, and through work on the Tutorial-based discussion, self-directed study, practical exercises, and through work Methods of Assessment Coursework assignments Coursework assignments Coursework assignments Coursework assignments

5 on the Learning Outcomes: Transferable Skills On the completion of this course successful students will be able to: Teaching/Learning Methods and Strategies Methods of Assessment On successful completion of this programme students will have gained Tutorial-based discussion, practical exercises, Coursework, oral presentations, or increased competence in: coursework assignments, through work on the Critical, analytical and creative thinking. Oral communication and in writing scientific documentations. Handling various types of IT resources. Time management. Team work. Tutorial-based discussion, practical exercises, coursework assignments, through work on the Tutorial-based discussion, practical exercises, coursework assignments, through work on the Tutorial-based discussion, practical exercises, coursework assignments, through work on the Tutorial-based discussion, practical exercises, coursework assignments, through work on the Coursework, oral presentations, Coursework, oral presentations, Coursework, oral presentations, Coursework, oral presentations, Learning Outcomes: Knowledge and Understanding On the completion of this course successful students will be able to: Teaching/Learning Methods and Strategies Methods of Assessment Explain how genetics and genomics contribute to medicine and science.. Communicate the principles of cell biology.

6 Perform statistical analyses and interpret the output from such analyses. Explain basic principles of statistical and machine learning methods. Utilise the basic elements of programming languages such as R. Elucidate the practical steps involved in performing a microarray, massively parallel sequencing or proteomic profiling analysis. Develop computational solutions for image interpretation and analysis

7 Communicate the importance of data integration and methods to deal with complex systems and associated data Develop solutions for the quantitative and statistical analysis of medical images from in house course materials and also from journal articles Learning Outcomes: Subject Specific Skills On the completion of this course successful students will be able to Teaching/Learning Methods and Strategies Methods of Assessment Select, apply and interpret statistical methods in the analysis of medical data. Interrogate relevant online resources for efficient data retrieval and analysis. Utilise comprehensive programming skills. Formulate and devise new algorithmic solutions for problems arising from biomedical research. Utilise a variety of existing databases and structure prediction tools in biomedical research.

8 Programme Requirements Module Title Introductory Cell Biology and Computational Analysis Scientific Programming & Statistical Computing Module Code Level/ stage Credits Availability Duration Pre-requisite Assessment S1 S2 Core Option Coursework % Examination % SCM weeks None No Assessment SCM weeks None Coursework - Genomics and Genetics SCM weeks None Oral presentation- 40% Written Assignment - 60% Analysis of Gene Expression SCM weeks None Essay - 80% Presentation - 20% Computational Diagnostics SCM weeks None Coursework (2) - Statistical Learning and Genomics SCM weeks None Coursework (2) - Bioimaging Informatics SCM weeks None Coursework - SCM Full Year None - Approved by Director of Education: Print Name: Professor Graham McGeown Signature Date: 1 September 2014.