The aim of this programme is to provide individuals with a platform to explore, analyse and interpret contemporary biological data. This course offers Master’s level instruction in Bioinformatics with a focus on genomic bioinformatics.
The aim of this programme is to provide individuals with a platform to explore, analyse and interpret contemporary biological data. This course offers Masters level instruction in Bioinformatics with a focus on genomic bioinformatics. You will develop key skills for the analyses of omics data including genomics data from next generation sequencing technologies. Additional skills around emerging omics including metabolomics and proteomics will also be developed.
This programme has been designed with the needs of academic research, biotechnology and the pharmaceutical and health care industries in mind. We will provide instruction in computational and statistical biosciences and students will foster these additional complementary skills required to enable individuals to work effectively within a multidisciplinary bioinformatics arena.
Aims of the Programme
- To provide you with the interdisciplinary practical skills and knowledge of computational and statistical biosciences to prepare them for challenging careers in academic research, biotechnology, the pharmaceutical and health care industries;
- To introduce the commonly exploited computational, statistical and analytical approaches to post genomic biology and genetics;
- To develop skills to understand and critically evaluate research methodologies and conclusions in order that they can make sound judgements about the applicability of these techniques to their own research;
- To develop competency in both the design and analysis of studies and the effective extraction of information in genetics, genomics and other biosciences coupled with the ability to communicate the information, results, issues and ideas to audiences of both a specialist and non-specialist background;
- To prepare and provide guidance to perform an original piece of research within the specialist area in which they wish to pursue their career. This area of the course also aims to strengthen your ability to take the initiative and responsibility for planning and carrying out tasks and demonstrate original thought in tackling and solving problems as would be necessary in employment.
This course was first established over a decade ago in response to the emerging informatics needs of the genetics and genomics communities following the completion of the first drafts of the human genome project. Subsequent advances in research technologies and analytic approaches have dictated the continuing evolution of this programme to provide contemporary instruction in these new essential skills.
Providing a strong platform for students entering from the biological, mathematical or computational sciences, this course provides modules in core complementary areas such as in computation/scripting, statistics and molecular biology; the fundamental building blocks necessary to succeed in bioinformatic analysis and interpretation.
As an introduction – you will be taught essential organisational and coding skills required for effective bioinformatics and biostatistical analysis.
One of the unique components of this course is the extended instruction in statistics provided by the Statistics for Bioinformatics and Genetic Epidemiology module.
You will also be introduced to the molecular and cellular biology behind the data. This is invaluable if you are entering from a non-life sciences background to make informed decisions around data interpretation.
You will extend your bioinformatics studies by focusing on next generation sequencing technologies and other developing omics platforms such as proteomics and metabolomics.
We are committed to developing transferable skills and to improving graduate employability. We want highly capable graduate informaticians who can fulfil the growing bioinformatics needs of local, national and international employers.
|Next intake||September 2019|
|Other ways to study this course|
In addition to satisfying the requirements of the Cardiff University General Entrance Requirements, meeting English language requirements of IELTS at least 6.5 and at least this in each subsection, at the discretion of the relevant Board of Studies, applicants will:
• normally possess a minimum of an upper second-class (or equivalent) primary degree in a bioscience or computing or mathematics/statistics.
• Graduates in other subject areas with a degree awarded by a recognised Institution will be considered by the course Admissions Committee.
At the discretion of the Board of Studies, non-graduates whose relative lack of formal qualifications or graduates who were awarded a lower-second class (or equivalent) degree may be accepted for study by compensation for their relevant work experience. The candidate must provide evidence that they have held, for a minimum period of two years, a position of responsibility relevant to the programme.
Please note: the programme requires a minimum number of students in order to run. Places are limited and early application is recommended. The closing date for applications is 31 July. If student numbers are not met by the 31 July we cannot guarantee that the programme will run.
Find out more about English language requirements.
Applicants who require a Tier 4 visa to study in the UK must present an acceptable English language qualification in order to meet UKVI (UK Visas and Immigration) requirements
Both full-time and part-time students register initially for the MSc Bioinformatics and Genetic Epidemiology
A Postgraduate Certificate exit point is available for students successfully completing 60 credits of the taught element (module restrictions apply).
A Postgraduate Diploma exit point is available for students successfully completing 120 credits of the taught element (module restrictions apply).
The full-time MSc is delivered across a single year consisting of two stages – the taught stage (T) and the research dissertation stage (R). The taught stage of the programme lasts until the end of the Spring Semester and consists of six 20-credit modules, totalling 120 credits, at Level 7. The dissertation stage of the programme runs through the Summer Semester and includes a dissertation of 60 credits at Level 7, to achieve a combined total of 180 credits at Level 7 to complete the MSc programme.
At the end of the taught stage (T), students who have obtained a minimum of 120 credits at Level 7 shall be eligible either for the exit award of Postgraduate Diploma OR to progress to the dissertation stage (R) of the programme.
Your dissertation will embody the results of your prior project work. The subject of each dissertation will be approved by the Chair of the Board of Studies concerned or his/her nominee. The dissertation will be assigned 60 credits and be weighted 50% for calculating the final mark.
The full-time course will be delivered across a single year. The course will be assessed using coursework and via student presentations. In the Autumn Semester, you will first take a 5-week 20-credit module designed to develop and reinforce necessary computational and scripting skill for data handling and analysis; this will include elements on command-line, high-performance computing, scripting languages and data visualisation.
From the middle of the Autumn Semester, you will receive concurrent delivery of 2x 20-credit modules in statistical methods and introduction to bioinformatics approaches and resources. These 2 courses will be delivered over 10-weeks and extend into the Spring Semester.
In the Spring Semester, you will undertake a 20-credit case-study extending over 10-weeks. This will include taught elements in research skills and involve working with a client with real data. Midway through the Spring Seminar you will undertake 2x 20-credit modules focusing on genomic, transcriptomic, proteomic and other omics data analysis techniques. These modules will run in series, and will be complete at the end of the Spring Semester.
Finally, in the Summer Semester, you will undertake a 15-week research project leading to the production of your research dissertation.
The modules shown are an example of the typical curriculum and will be reviewed prior to the 2019/20 academic year. The final modules will be published by September 2019.
The course will be delivered across a single year. The course will be assessed using coursework and via student presentations. In the Autumn Semester, you will first take a 5-week 20-credit module designed to develop and reinforce necessary computational and scripting skill for data handling and analysis; this will include elements on command-line, high-performance computing, scripting languages and data visualisation. From the middle of the Autumn Semester, Students will receive concurrent delivery of 2x 20-credit modules in statistical methods and introduction to bioinformatics approaches and resources. These 2 courses will be delivered over 10-weeks and extend into the Spring Semester. In the Spring Semester, students will undertake a 20-credit case-study extending over 10-weeks. This will involve working with a client and real data. Midway through the Spring Seminar the students will undertake 2x 20-credit modules focusing on the analysis and interrogation of genomic, transcriptomic, proteomic and other contemporary omics data. These modules will run in series, and will be complete at the end of the Spring Semester. Finally, in the Summer Semester, students will undertake a 15-week research project leading to the production of their research dissertation.
|Module title||Module code||Credits|
|Computing for Bioinformatics and Genetic Epidemiology||MET581||20 credits|
|Statistics for Bioinformatics and Genetic Epidemiology||MET582||20 credits|
|Introduction to Bioinformatics||MET583||20 credits|
|Case Studies in Bioinformatics and Biostatistics||MET584||20 credits|
|Next Generation Sequencing||MET585||20 credits|
|Protein Biology and Omics||MET586||20 credits|
|Dissertation: Bioinformatics||MET591||60 credits|
|Dissertation: Bioinformatics||MET591||60 credits|
How will I be taught?
The programme is delivered as face-2-face learning. You will find course materials, links to related materials and assessments via Cardiff University’s Virtual Learning Environment (VLE) ‘Learning Central™’.
- Each taught Module will be delivered using a small-group classroom-model (< 20 participants per session).
- You will be expected to commit to approximately 200 hours of effort per module; from this you will receive approximately 60 hours of face to face contact. This will be delivered through approximately 20 teaching sessions. Each teaching session will be 3-hours with appropriate breaks.
- Teaching sessions will include a combination of elements such as theory (blackboard learning), computer practical, group-work and tutorial. We believe that to achieve learning outcomes for a bioinformatics-based course it is not often useful to deliver extensive ‘lecture-only’ sessions. We believe you will learn through application and interaction. However, where appropriate, some theory will be delivered as lectures.
- Each session will take a variety of forms, but the overall structure will be to achieve 2-3 key learning outcomes per session via introduction to key concepts, conveying relevant up-to-date information and exposure to research methodology and application.
- In the teaching sessions, we will provide feedback on formative assessments and course content. In these elements, you will have the opportunity to discuss themes or topics, to consolidate and get feedback on your individual learning and to develop skills in oral presentation. Communication skills will be developed in tutorials, where you will make individual contributions to group study; for example, by summarising a formative assessment or outcomes from group sessions.
- You will practise and develop theory, intellectual skills, team-work and presentational skills by participating in diverse learning activities, such as solving bioinformatics problems, small-group discussions, oral presentations, independent research tasks and written assignments.
- You may be taught alongside students from other Programmes, including the MSc in Bioinformatics and Genetic Epidemiology as well as students undertaking Continuing Professional Development (CPD).
How will I be supported?
As part of the induction process, we host a 2-hour welcome session for all students – so that you can meet your fellow students, the course management and the teaching team. We also provide an induction session related to tutoring and library services and resources. All students will be provided with an up-to-date student handbook, provided in the appropriate college format.
A personal tutoring system exists to guide you through the course with the aim of encouraging both academic success and personal well-being and development. At the start of the course you will meet with your Personal Tutor to ascertain and discuss your perceived areas of strength and weakness (using the pre-course questionnaire answers as a starting point for discussion). This will cover subject areas of study, English language capability and track record in past examinations.
Specific learning differences, disability and/or medical conditions will be identified and the Programme Director will follow university procedures and guidance on this. We are committed to equality for the English and Welsh languages and if you are a Welsh speaker you may request to have a Welsh-speaking Personal Tutor.
From an academic viewpoint, your tutor is there to provide guidance on any general academic issues/questions arising from the course and to encourage you to pursue self-directed learning. The role of the tutor is not to read or mark assignment drafts, but they can communicate with you about any topic that you may find challenging. Detailed feedback from the Module Team is given to students once an assignment has been marked. You are welcome to discuss this feedback with your tutor
During the MSc dissertation stage, each student will be allocated a Dissertation Supervisor who will guide their planning and advise them as they complete their projects.
How will I be assessed?
Formative feedback is feedback that does not contribute to progression or degree classification decisions. The goal of formative feedback is to improve your understanding and learning before you complete your summative assessment. More specifically, formative feedback helps you to:
- identify your strengths and weaknesses and target areas that need work;
- help staff to support you and address the problems identified – allowing targeted strategies to be developed for your improvement.
- The development of skills in data sciences requires practice. You learn through your successes and failures. You will receive short questions and tasks as part of the teaching sessions. These formative tasks will help reinforce your learning and provide useful feedback to the course co-ordinators about where we can improve and focus resources. Although formative marks will not contribute to progression or degree classification decisions, appropriate investment in these tasks will assist the summative elements of the Module.
- Summative feedback is feedback that contributes to progression or degree classification decisions. The goal of summative assessment is to indicate how well you have succeeded in meeting the intended learning outcomes of a Module and will enable you to identify any action required to improve. To maintain consistency and standards, markers of assessments make use of pre-defined marking scheme for each assignment. All feedback should directly link to the Module grading / assessment criteria.
Our preferred feedback methods include coursework and presentation. Students will generally receive two coursework elements; a theory-driven review paper (e.g. a “compare and contrast” essay), and an applied research project. Students will be required to present their findings of their research to their peers and examiners as part of the summative assessment process.
Formative feedback will be communicated to the students through electronic and written means in a timely manner. Additional feedback, to the group, will be provided as part of the tutorial components of the teaching sessions. Summative feedback on assessment will be delivered within the timeframe set by the University.
Overall Assessment Strategy
The implemented assessment strategy will evaluate;
- Knowledge and Understanding: Coursework will include data analysis, reviews and programming exercises and will be assessed through written reports.
- Intellectual skills: Assessment of intellectual skills will be via problem-based exercises/assignments, and case study reports. Evaluation of your understanding of the material will also be assessed through presentation. Where applicable these skills will be assessed via the dissertation, interview research project and critical review.
- Practical skills: Practical skills will generally be assessed via practical based assignments. Where applicable these skills will need to be demonstrated within the dissertation, research project and critical review. Assessment of such practical skills will be incorporated into the final grading for the dissertation.
- Transferable/key skills: Assessment of written communication skills will be via report writing and the dissertation (where applicable). Oral presentations comprise part of the assessment on selected modules and team working skills will be assessed during the collaborative assignment.
- MSc Dissertation: The dissertation stage will be assessed based on the final dissertation. The dissertation shall be assigned 60 credits and, in combination with the taught stage(s), shall be weighted 50% for calculating your final mark. Expectations for the format, submission and marking of the dissertation will follow current Senate Assessment Regulations, supplemented where appropriate with additional requirements of the Programme/School/College and any specific requirements arising from the nature of the project undertaken. We will provide guidelines to the students to how assessments will be made for their dissertations.
We are committed to equality for the English and Welsh languages and if you are a Welsh speaker you may request to have a Welsh-speaking Personal Tutor, and undertake your assessments through the medium of Welsh.
What skills will I practise and develop?
Upon completing the Programme, you will be able to demonstrate:
- a systematic understanding of the principles of statistics, biosciences and computer science that are the foundations of genetic epidemiology and bioinformatic;
- a knowledge of current bioinformatics software and databases and their applicability to solving bioinformatics problems in the field and in their own research;
- a keen awareness, understanding and critical appreciation of the wide variety of statistical methods available to analyse genetic data together with appreciation of the issues involved in study design;
- a knowledge of statistical software used to analyse omics data and the situations in which the packages should/should not be applied;
- integrate acquired knowledge and understanding with practical skills resulting in a sound approach to problem solving;
- synthesise information from a variety of sources at the forefront of Genomics and Bioinformatics;
- critically evaluate different approaches to solving problems and to demonstrate their applicability, strengths and weaknesses;
- formulate hypotheses and use analytical skills to test these hypotheses. Interpret the results and make decisions in the light of all available information in possibly complex and unpredictable situations;
- undertake a semi-independent piece of original research or critical review demonstrating self-initiative, responsibility for planning and carrying out tasks, tackling and solving research problems;
- the ability to assess the correct type of analysis for a dataset and to perform it using an appropriate computer package;
- competency in accessing and using bioinformatics tools from a variety of sources;
- computer programming skills and competency in good program design;
- the ability to design, implement and analyse algorithms for bioinformatics applications;
- the ability to produce case study reports on major topics within the fields of bioinformatics and biostatistics;
- competent research skills such as literature searches;
- work independently, to manage his/her own time and to take responsibility for learning required for continued professional development;
- effectively manage individual tasks and larger scale projects identifying and resolving possible obstacles;
- work as part of interdisciplinary teams, identifying individual’s strengths and weaknesses and allocating work accordingly to meet the team’s objectives;
- competently use information technology such as e-mail, word-processors, software tools and the world-wide web;
- transfer computing skills and concepts to subsequently unfamiliar packages/languages/databases;
- present information, understanding and arguments using effective communication (written and oral) with awareness of intended audience.
This programme has been designed with the needs of academic research, the biotechnology, pharmaceutical and health care industries in mind. Instruction in computational and statistical biosciences will enable individuals to work effectively within a multidisciplinary bioinformatics arena.
Since 2004, of the MSc students who have successfully graduated, within one year;
•24% have continued their education by entering PhD Programme in Bioinformatics / Biostatistics
•17% have continued their education by entering PhD Programme in other areas of biomedicine
•10% have continued their education by entering further Masters study/PGCE/medical degrees
•20% have gained employment in Bioinformatics and or Biostatistics
•29% have gained employment in other scientific jobs often with a data analysis context
This Masters programme enables you to demonstrate you are taking the opportunity to develop your abilities in critical analysis, problem-solving, decision-making, finding and using evidence and in dealing with complex issues. The programme is not a substitute for a formal specialty training programme but studying at this level should help successful students demonstrate numerous academic skills that should be highly regarded in relation to their career development and progression. In particular, the programme offers opportunities to demonstrate the development of knowledge and skills in relation to the application of evidence-based medicine and the potential enhancement of services and governance frameworks. As such, it should provide evidence of commitment and potential that may assist you in relation to taking on greater responsibilities or perhaps seeking management, research, scholarship, or leadership roles.
UK and EU students (2019/20)
More information about tuition fees and deposits, including for part-time and continuing students.
EU students entering in 2019/20 will pay the same tuition fee as UK students for the duration of their course. Please be aware that fees may increase annually in line with inflation. No decisions regarding fees and loans for EU students starting in 2020/21 have been made yet. These will be determined as part of the UK's discussions on its membership of the EU and we will provide further details as soon as we can.
Students from outside the EU (2019/20)
More information about tuition fees and deposits, including for part-time and continuing students.
It is expected that all students will have their own laptop computer. Please liaise with the Programme Director before purchasing any hardware for further advice on the right choice of machine. You will also require regular access to a reliable broadband internet connection. No additional costs are necessary with this Programme
Will I need any specific equipment to study this course/programme?
You will need to provide a reliable computer with appropriate Internet access, with up-to-date virus and malware protection
Course material will be provided in portable-document-format and Microsoft PowerPoint.
Word processing software, compatible with Microsoft Word, will be required to complete the summative and formative tasks and assessments. Other software may also be useful at some points in the programme for conducting data collection/analysis, for instance Microsoft Excel, or producing presentations, for instance Microsoft PowerPoint.
Cardiff University will provide all learning materials appropriate to each module and curriculum stage and an online learning environment through which you will access course materials and communicate with tutors and other students. You will also have access to a vast array of journals and electronic and hard copy text books related to your studies through the University Library.