Bioinformatics and Genetic Epidemiology (MSc)
- Duration: 3 years
- Mode: Part time
Why study this course
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 and Genetic Epidemiology with a focus in genetic epidemiology.
COVID19 UPDATE The mode of delivery for this programme (2020/1 entry) has been altered due to the COVID19 pandemic. To ensure the safety and wellbeing of all our students and staff, we have taken the decision to convert the first term of this programme to fully online distance learning. We will review the pandemic situation constantly and make an informed decision about whether the second term will also be delivered online or face to face as soon as we are able to. If you have any questions at all, please contact the Postgraduate Medical Admissions Office at PGTMedAdmissions@cardiff.ac.uk.
With a focus on genetic epidemiology, this programme is ideal for graduates from a life sciences, mathematics, or computer sciences discipline.
It will provide you with the skills and knowledge of computational and statistical biosciences to prepare you for a challenging career in academic research, biotechnology, or the pharmaceutical and healthcare industries.
Bioinformatics is the field of study that utilises computational tools to understand biology. Genetic Epidemiology is the study of how genetic factors play a role in determining health and disease, and their interplay with the environment. As well as developing core skills in computational and statistical biosciences, you will focus gene discovery approaches including GWAS, explore copy-number variation (CNV) analysis, and post-GWAS approached such as pathway/network, gene-set and polygenic epidemiological methods.
This programme has been designed to meet the growing demand from academic research, biotechnology and the pharmaceutical and health care industries for capable informaticians with bioinformatics skills. We will provide instruction in computational and statistical biosciences and you will foster the additional complementary skills required to enable you to work effectively within a multidisciplinary bioinformatics arena.
Aims of the Programme
- 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 that allow you to make sound judgements about the applicability of these techniques to your 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 you wish to pursue your career
This course was first established over a decade ago in response to the completion of the first drafts of the human genome project and the subsequent informatics needs of the genetics and genomics communities. Ongoing advances in genomic technologies and analytic approaches have dictated the continuing evolution of this programme to provide contemporary instruction in new essential skills.
Our course is accessible to students with primary degrees in mathematics, life sciences or computing. Modules in core complementary areas such as in computation/scripting, statistics and molecular biology provide the fundamental building blocks necessary to succeed in bioinformatic analysis and interpretation.
In the Spring Semester, you will undertake a 20-credit case-study. This will include taught elements in research skills and involve working directly with a client using real data. You will be embedded in one of the many research centres across campus and gain valuable experience in delivering bioinformatics projects for research programmes. The resulting data will also be presented alongside your peers at our case-study poster sessions.
You will be taught essential organisation and coding skills and given extended instruction in statistics. If you are not from a life sciences background, we will introduce you to the biology behind the data and help you make informed decisions around data choice and interpretation.
We're committed to delivering programmes that are innovative and relevant, providing the best learning outcomes and career prospects for our students. In light of this, we're currently reviewing some elements of this programme. As such, the details shown are subject to change and indicative only. You can still apply now. We'll update this page and contact all offer holders when the review is complete to confirm any changes.
In addition to satisfying the requirements of the Cardiff University General Entrance Requirements, meeting English language requirements of IELTS 7.5 with at least 6.5 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.
You will also need to provide a personal statement. Your personal statement should provide information that is relevant to your application for admission to tell us why you wish to follow this programme, what benefits you expect to gain from it, and what skills and experience you possess which make you a suitable applicant. You must use the questions below as headings in your document:
1. What motivates you to apply for this course?
2. How would you describe your computer literacy and what coding experience do you have?
3. How would you describe your knowledge of statistics?
4. How would you describe your knowledge of molecular biology and genetics?
5. If you have previously applied for this course and were unsuccessful, describe what further experience you have gained which may strengthen your application.
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 Student visa to study in the UK must present an acceptable English language qualification in order to meet UKVI (UK Visas and Immigration) requirements.
You are not required to complete a DBS (Disclosure Barring Service) check or provide a Certificate of Good Conduct to study this course.
If you are currently subject to any licence condition or monitoring restriction that could affect your ability to successfully complete your studies, you will be required to disclose your criminal record. Conditions include, but are not limited to:
- access to computers or devices that can store images
- use of internet and communication tools/devices
- freedom of movement
- contact with people related to Cardiff University.
Both full-time and part-time students register initially for the MSc in Bioinformatics and Genetic Epidemiology.
You may exit the course with a Postgraduate Certificate if you successfully complete 60 credits. Alternatively, you may leave with a Postgraduate Diploma on successful completion of 120 credits. Module restrictions apply in both cases.
The part-time MSc is delivered across three years consisting of three stages – the first taught stage (T1), the second taught stage (T2) and the research dissertation stage (R).
The first taught stage of the programme lasts one academic year and consists of three 20-credit modules, totalling 60 credits, at Level 7. The second taught stage lasts one academic year and consists of three further 20-credit modules (one of which is a case study), totalling 60 credits, at Level 7. The dissertation stage of the programme runs one academic year and includes a dissertation of 60 credits at Level 7, to achieve a combined total of 180 credits to complete the MSc 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 modules shown are an example of the typical curriculum and will be reviewed prior to the 2022/23 academic year. The final modules will be published by September 2022.
The part-time course will be delivered across three years.
Year one 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 undertake a 20-credit module in statistical methods and this will be delivered over 10-weeks and extend into the Spring Semester.
In the Spring Semester, you will undertake a 20-credit genetic epidemiology module focusing on association and linkage. This module will run in into the Summer Semester.
|Module title||Module code||Credits|
|Computing for Bioinformatics and Genetic Epidemiology||MET581||20 credits|
|Statistics for Bioinformatics and Genetic Epidemiology||MET582||20 credits|
|Genetic Epidemiology: Association and Linkage||MET587||20 credits|
In the second year from the Autumn Semester, you will study a 20 credit module - an introduction to bioinformatics and this will extend into the Spring Semester. A second 20 credit module is a case study which runs in the Spring Semester and a third 20 credit module in Post-GWAS genetic epidemiology extending into the Summer Semester,
In year 3 you will undertake a 15-week research project leading to the production of your research dissertation.
The University is committed to providing a wide range of module options where possible, but please be aware that whilst every effort is made to offer choice this may be limited in certain circumstances. This is due to the fact that some modules have limited numbers of places available, which are allocated on a first-come, first-served basis, while others have minimum student numbers required before they will run, to ensure that an appropriate quality of education can be delivered; some modules require students to have already taken particular subjects, and others are core or required on the programme you are taking. Modules may also be limited due to timetable clashes, and although the University works to minimise disruption to choice, we advise you to seek advice from the relevant School on the module choices available.
Learning and assessment
How will I be taught?
The programme is delivered face-to-face in Cardiff. You will have access to course materials, links to related materials and assessments via our virtual learning environment.
Each 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 application and interaction are crucial for a bioinformatics-based course and there will be an emphasis on these. 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.
How will I be assessed?
Our preferred assessment methods include coursework and presentation. You will generally receive two coursework elements; a theory-driven review paper (e.g. a “compare and contrast” essay), and an applied research project.
Coursework and assessments will vary depending on the module and will be designed to test your knowledge and understanding of topics, as well as key intellectual skills, practical skills, and transferable skills.
Coursework may include data analysis, programming exercises, written reports, problem-based exercises, case studies, practical assignments, slide and poster presentations.
Formative assessments and feedback
These are assessments that do 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, it helps you to identify your strengths and weaknesses, and helps staff to support you in improving these areas.
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 assessments and feedback
These assessments contribute 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.
How will I be supported?
During induction you will be given an introductory session to help you make the most of tutoring, library services, and available resources.
You will be supported by a personal tutor with the aim of encouraging both academic success and personal well-being and development.
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. You will receive detailed feedback from the module team on completed assignments and you will be able to discuss this feedback with your tutor
During the MSc dissertation stage, you will also be allocated a Dissertation Supervisor who will guide your planning and advise you as you complete your project.
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 bioinformatics
- 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
Tuition fees for 2022 entry
Due to the duration of this programme only Welsh and EU domiciled students who meet residency requirements (English domiciled students are excluded) are eligible for a postgraduate loan. See more information about eligibility for UK Government Postgraduate loans.
Your tuition fees and how you pay them will depend on your fee status. Your fee status could be home, island or overseas.
Fees for home status
Students from the EU, EEA and Switzerland
If you are an EU, EEA or Swiss national, your tuition fees for 2022/23 be in line with the overseas fees for international students, unless you qualify for home fee status. UKCISA have provided information about Brexit and tuition fees.
Fees for island status
Learn more about the postgraduate fees for students from the Channel Islands or the Isle of Man.
Fees for overseas status
More information about tuition fees and deposits, including for part-time and continuing students.
Financial support may be available to individuals who meet certain criteria. For more information visit our funding section. Please note that these sources of financial support are limited and therefore not everyone who meets the criteria are guaranteed to receive the support.
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.
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.
We’re based in one of the UK’s most affordable cities. Find out more about living costs in Cardiff.
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. That is why 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 you to work effectively within a multidisciplinary bioinformatics arena.
Our first cohort of five students enrolled to the reconfigured MSc in September 2018. As of July 2019 one student has accepted a PhD post, and two have accepted research associate posts in bioinformatics. We eagerly await the result of upcoming interviews!
Historically, from 2004-2017 this programme has seen approximately 24% students enter a PhD directly related to bioinformatics, 17% students entered other PhD in other areas of biomedicine; 10% entered MSc/PGCE/medical degrees; and approximately 49% gained employment related to bioinformatics, biostatistics or other data science .
Other course options
HESA Data: Copyright Higher Education Statistics Agency Limited 2021. The Higher Education Statistics Agency Limited cannot accept responsibility for any inferences or conclusions derived by third parties from its data. Data is from the latest Graduate Outcomes Survey 2018/19, published by HESA in June 2021.