Study options
- Starting in
- September 2025
- Location
- Charterhouse Square
- Fees
- Home: £12,850
Overseas: £29,950
EU/EEA/Swiss students
What you'll study
Biomedical science is increasingly data driven, as new bioanalytical techniques deliver ever more data about DNA, RNA, proteins, metabolites and the interactions between them in the whole tissue and single-cell levels. A wide range of state-of-the-art techniques in the field of cancer genomics and data science for example modelling, data integration, machine learning and AI is required to analyse multi-layer large scale cancer datasets and derive meaningful interpretable results.
However there is a serious shortage of well-trained bioinformaticians, computational biologists and data analysts who have the relevant skillset and experience in real world biomedical and cancer data. This programme is designed to fill the gap between research and employment demands and student training, offering up-to-date modules focusing on “big-data” analyses and enabling these through use of high-performance computing, together with cutting edge research projects and practical training using real world cohort data.
You’ll be taught by academics who are actively engaged in developing bioinformatics and computational tools, and applying them in cancer and medical research areas such as genomics, proteomics, evolution, modelling and biomarker discovery. We have an extensive network of academic and industrial collaborators around the UK, who contribute to teaching, co-supervise research projects and provide employment opportunities.
Watch our video to find out more about studying at the Barts Cancer Institute.
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Structure
- Eight compulsory modules
- Research project
Compulsory/Core modules
This module provides an introduction of computer programming of two most widely used programming languages in biomedical and cancer data science, R and Python. It provides and trains students with the introduction of programming concepts, how to manipulate data frames, use regular expression, write/use functions and perform object oriented programming in both languages. This module consists of both lecture and practical sessions, ensuring students understand and solve complex problems using real cancer data as examples.
This module is focussed on teaching the most up-to-date pipelines and tools to analyse high-throughput omics data, including RNA-seq, DNA-seq, ChIP-seq, DNA methylation and proteomics. Students will gain hands-on experience of analysing real-life patient and experimental data through use of high-performance computing. Students will obtain competence in all aspects of the analytic workflow including data handling, QC, data manipulation, interpretation and visualisation. This module is taught through both lectures and praticals, ensuring students are able to analyse diverse types of omics data.
This module is focussed on teaching advanced bioinformatics and computational skills to analyse omics data to address important cancer research and clinical questions, such as tumour evolution and immunogenomics. Students will further their understanding of cancer genomics and transcriptomics, and learn essential skills and workflows of dissecting tumour clonal architecture and evolutionary patterns, deconvolution of immune cell infiltration, and deciphering tumour immunogenicity. This module is taught through both lectures and praticals, ensuring students are able to process and analyse data, as well as produce and interpret meaningful results.
This module will give an introduction to theoretical modeling in biology. It will introduce classical deterministic models, e.g. exponential growth, Lotka-Volterra and SIR models, it will introduce basic ideas of probabilities and stochastic modeling leading up to the Luria-Delbrück experiment and its many applications to virus, bacteria and cancer evolution. Although, we will discuss concrete mathematical models, the discussion will be on a high level and we expect no advanced mathematical background of students.
The main focus of the module is for students to learn about different single-cell technologies, focusing on single-cell assays for gene expression, chromatin accessibility, genomics and spatial methods and how integration of different modalities can provide a more holistic view of cell and tissue phenotypes, cell-cell interactions and cell state dynamics. The module will introduce these technologies, their advantages and limitations, and will provide examples of how they can be applied in a real biological context. The module will have a specific focus on computational analysis and integration of real data produced by these technologies. Students will gain hands-on experience of analysing real-life patient and experimental data through use of high-performance computing. The students will acquire analytical skills that will improve their employability in research and industry.
This module provides an introduction to data science, machine learning (ML) and AI, focussing specifically on their applications of analysing omics (e.g., transcriptomics and proteomics) and imaging data to identify novel features in biomedical and cancer research. Lectures cover the methods, algorithms, workflows and application examples for different ML/AI techniques, as well as research / clinical questions they can address. In practical sessions, students will gain hands-on experience in applying ML/AI techniques to different data types to generate robust and meaningful results.
This module provides an opportunity to further develop and apply skills learned during the taught modules of MSc Cancer Genomics and Data Science, by conducting a novel piece of bioinformatics and computational work, typically within an active research group either within BCI, QMUL or at a partner organisation. The specific nature of each project will be determined through discussions between the student, the course director and the project supervisor but will involve applying analytical, investigative and communication skills and utilizing a range of bioinformatics and computational methods and tools in a cutting edge area of biological or biomedical research. This serves as excellent preparation for future employment or PhD.
This module will provide detailed teaching on the principles and interpretation of large scale genomic and proteomic approaches to cancer, including: - The application of genomic technology - The working principles of expression array and genotype array technology - The principles of bio-statistical analysis of genomic data - The advantages and limitations of the various genomic approaches described - The use of proteomic approaches in studying cancers.
Elective modules
Assessment
- 67%% Modules
- 33%% Research project
Assessment will be, but not limited to, short and long answer coursework, multiple choice questions, presentations and research dissertation. Assessment has been designed to develop and assess a broad range of skills that will be essential for students in their future careers. There will also be a 10,000 word dissertation.
Research project
You will undertake a research project focused on cancer genomics, bioinformatics, computational biology and data science.
Teaching
You'll be taught through a variety of teaching and learning methods including lectures, practicals, seminars, self-directed learning, live Q&A sessions, case presentations, and reading lists of books and journal papers.
Research projects will be supervised by a Barts Cancer Institute (BCI) or Queen Mary researcher.
If you study part-time, the modules you’ll need to complete the programme will be spread over two years.
We take pride in the close and friendly working relationship we have with our students. You’ll be assigned an Academic Mentor, who will guide you in both academic and pastoral matters throughout your time at Queen Mary.
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Where you'll learn
Facilities
- Access to specialised scientists and clinicians
- Full access to our library and online resources
- Mentors for non-programme related support, including careers advice
- A dedicated Teaching Centre for administrative support
Campus
Barts Cancer Institute is situated in Queen Mary’s beautiful Charterhouse Square campus. This is where you’ll find our research laboratories, clinical trials centre and where most teaching takes place. The Square is within minutes of the Barts Cancer Centre at St Bartholomew’s Hospital, one of the most advanced cancer centres in Europe.
Rich in history and diversity, Charterhouse Square is a bustling centre of energy and activity. You will get to experience both old and new London.
About the Institute
Barts Cancer Institute
Barts Cancer Institute (BCI) is one of the top five cancer research centres in the UK and is one of 14 Cancer Research UK Centres of Excellence.
Our research goal is to prevent cancer and develop better diagnostic techniques and treatments. We are generously supported by research councils, industry and a number of charities, with the grants awarded totalling in excess of £15m per annum.
BCI is part of Queen Mary’s medical school, the Faculty of Medicine and Dentistry. The School is comprised of two world-renowned teaching hospitals, St Bartholomew's and The Royal London, which make an outstanding contribution to modern medicine. Queen Mary is ranked joint seventh in the UK for the quality of our research (REF 2021).
We are a member of the Russell Group of leading research universities in the UK and proudly hold an Athena Gold Award in recognition of our commitment to gender equality.
Career paths
You’ll leave this programme with competence in state-of-the-art analytic workflows and hands-on experience of a wide range of real-life cancer and medical data, so that you will be ready to meet research and industry needs after graduation.
There is high demand for well-trained bioinformaticians and computational biologists to manage, analyse, integrate and visualise “big data”, both in academia and industry. Bioinformatics and data science skills are highly transferable, allowing skilled individuals to move to other sectors, such as data analytics, software development and quantitative finance.
We anticipate that graduates from this programme are likely to move into roles such as:
- Bioinformatician
- Data analyst
- Computational biologist
- Researcher
In both academia and industry, including large pharmaceutical companies, small and medium-sized enterprises, and start-ups.
- 91% of BCI postgraduate taught students in employment of further study (2020/21)
- 95% of BCI postgraduate taught students in highly skilled work or graduate study (2020/21)
Fees and funding
Full-time study
September 2025 | 1 year
- Home: £12,850
- Overseas: £29,950
EU/EEA/Swiss students
Unconditional deposit
Home: Not applicable
Overseas: £2000
Information about deposits
Part-time study
September 2025 | 2 years
- Home: £6,450
- Overseas: £15,000
EU/EEA/Swiss students
Unconditional deposit
Home: Not applicable
Overseas: £2000
Information about deposits
Queen Mary alumni can get a £1000, 10% or 20% discount on their fees depending on the programme of study. Find out more about the Alumni Loyalty Award
Funding
There are a number of ways you can fund your postgraduate degree.
- Scholarships and bursaries
- Postgraduate loans (UK students)
- Country-specific scholarships for international students
Our Advice and Counselling service offers specialist support on financial issues, which you can access as soon as you apply for a place at Queen Mary. Before you apply, you can access our funding guides and advice on managing your money:
Entry requirements
UK
Degree requirements
A 2:1 or above at undergraduate level in any subject, provided the degree contains satisfactory study of Mathematics and Statistics. Subjects likely to contain sufficient quantitative elements include Genetics, Genomics, Bioinformatics, Mathematics, Statistics, Engineering, and Computer Science.
Applications from those with less quantitatively oriented Natural Sciences degrees, such as Biology and Medicine, are welcome if they have focused on the more quantitative elements of those degrees.
Other routes
Applicants with a 2:2 degrees with relevant content and at least one year of relevant experience, for instance work in industry, analytics, diagnostic labs, scientific research etc, may be considered on an individual basis.
Find out more about how to apply for our postgraduate taught courses.
International
English language requirements
The English language requirements for our programmes are indicated by English bands, and therefore the specific test and score acceptable is based on the band assigned to the academic department within which your chosen course of study is administered. Note that for some academic departments there are programmes with non-standard English language requirements.
The English Language requirements for entry to postgraduate taught and research programmes in the Barts Cancer Institute falls within the following English band:
Band 4: IELTS (Academic) minimum score 6.5 overall with 6.0 in each of Writing, Listening, Reading and Speaking
We accept a range of English tests and qualifications categorised in our English bands for you to demonstrate your level of English Language proficiency. See all accepted English tests that we deem equivalent to these IELTS scores.
Visas and immigration
Find out how to apply for a student visa.