Cancer Genomics and Data Science

Key Information

  • Award: MSc
  • Modes of Study: Full Time / Part Time / Full Time - Distance Learning / Part Time - Distance Learning
  • Programme Director: Dr Jun Wang
  • Start Date: September 2024
  • Application Deadline: 31 July 2024

For more information and to apply, visit the Queen Mary University of London website:

Biotechnology bioinformatics concept of DNA and protein letter background, DNA and protein sequence 3d render.

The Cancer Genomics and Data Science programme at Barts Cancer Institute, Queen Mary University of London is new for 2022. The programme is suitable for graduates with a quantitative background, such as genetics, genomics, maths, physics, engineering and computer sciences; or with backgrounds in Biology, Medicine, or a relevant natural sciences subject with strong quantitative skills.

The introduction and application of high-throughput 'omic technologies in biomedical studies have accelerated our understanding and knowledge of human physiology and diseases, such as cancer. The growing use of molecular profiling to decipher genetic aberrations driving disease development and progression has led to an increase in ‘big-data’ projects. However, the supply of skilled and trained personnel to sufficiently manage, analyse, integrate and visualise 'big data' is lagging far behind the rapidly increasing demand.

Our new Cancer Genomics and Data Science programme aims to address this gap by teaching students state-of-the-art analytic workflows and allowing them to gain hands-on experience with a wide range of real-life cancer and medical data, so that they will be ready to meet research and industry needs after graduation.


Compulsory modules

  • Introduction to Human Genomics
  • Omics Techniques and their Application to Genomic Medicine
  • R and Python Programming in Biomedical Research
  • Omics Data Analytics and Practical Training
  • Computational Genomics, Transcriptomics and Evolution
  • Mathematical Modelling and Application
  • Single Cell Analytics
  • Machine Learning / AI and Application to Biomedical Research
  • Research project of bioinformatics, computational biology and data science
Full Time

Semester 1: September - December (60 credits taught modules)
Semester 2: January - March (60 credits taught modules)
Semester 3: May - August (60 credit research project)

Part Time

Year 1

Semester 1: September - December (30 credits taught modules)
Semester 2: January - March (30 credits taught modules)

Year 2

Semester 1: September - December (30 credits taught modules)
Semester 2: January - March (30 credits taught modules)
Semester 3: May - August (60 credit research project)


Semester 1: January      Semester 2: March/April       Semester 3: August resits

Each 15 credit module involves approximately 30 hours contact time and 120 hours of self-study.

Each 7.5 credit module involves approximately 15 hours contact time and 60 hours of self-study.

The Dissertation is carried out full time over 12 weeks with regular supervision from one of the Institute’s Research Centres. There may be some flexibility to arrange part-time dissertations for part-time students who are unable to commit to completing this full time. These are arranged on a case-by-case basis in consultation with the Programme Director.

Modes of study
Award and structure

MSc: 120 taught credits + 60 credit Dissertation

A variety of study options are available, these include Full Time (1 year) and Part Time (2 years), on-site or by Distance Learning.

Distance Learning

Distance learning is delivered via the University’s online learning platform - QMplus. Onsite lectures are recorded and made available to Distance Learning students, together with copies of the slides and other supporting teaching materials.
Dissertations will be supervised by a Barts Cancer Institute researcher and supervision meetings will be conducted by telephone or by Skype.


Posters and oral presentations will be delivered via Skype.
Examinations will take place online.

Entry Requirements

Academic Entry Requirements

Please visit the Queen Mary University of London website for full details on UK and International entry requirements. 

English Language Requirements

If you got your degree in an English speaking country or if it was taught in English, and you studied within the last five years, you might not need an English language qualification - find out more.

English language entry requirements for programmes within the Barts Cancer Institute

You may be able to meet the English language requirement for your programme by joining a summer pre-sessional programme before starting your degree.

How to Apply

All applications must be completed online via the Queen Mary University of London website:

Apply for the MSc (full time or part time)
Apply for the MSc Online (full time or part time)

As part of your application you will be required to provide the following documents:

  • Personal Statement
  • CV
  • 2 references (at least one of these must be an academic reference)
  • Copies of degree transcripts
  • English language results and certificate (if applicable)
Intercalating Applicants

Intercalating students must also apply via the Intercalated Degrees Admissions team. Please visit the Queen Mary website for more detailed information about the application process.

Fees & Funding
Full-time study - September 2024 | 1 year
Part-time study - September 2024 | 2 years

Note that fees may be subject to an increase on an annual basis - see details on the Queen Mary tuition fees page.

Funding and scholarships

Numerous funding options are available to Postgraduate students. For more information, visit the Queen Mary Funding a Masters webpage.


Due to receiving large volumes of successful applications, it is our department policy to request a deposit of £2000 from overseas students and £1000 from home/EU students in order to secure your place on the course. This will be payable on acceptance of an offer.

Programme Team

The programme is being developed and delivered by our specialists in the field

BEng Biological Engineering, MSc Quantitative Genetics and Genome Analysis, PhD
Dr Wang's research interests lie in applying bioinformatics and computational approaches to analyse large-scale cancer datasets to uncover novel diagnostic and prognostic features.
BSc Biochemistry, PhD
Professor Cutillas' research group uses unique proteomics and computational approaches to understand how cell signalling pathways driven by the activity of protein kinases contribute to the development of cancer.
MSc Bioinformatics, PhD
By integrating single-cell multi-omics data, imaging and computational methods, Dr Efremova's lab aims to dissect the cancer cell intrinsic traits and cell-cell communication networks that promote metastasis and therapy resistance.
BSc Biochemistry, PhD
Dr McClelland's research aims to understand the mechanisms that underlie numerical and structural chromosome aberrations in cancer at a molecular level, which also involves understanding how normal cells replicate and segregate their genomes.
Diploma Physics, PhD
Dr Werner's research combines mathematics, computer simulations and genomic information to study evolutionary processes, with the aim of understanding how a tumour’s evolutionary history is reflected in its genome.

Virtual Open Events

Find out more about our MSc programmes and have the opportunity to ask the Programme Directors questions at our Virtual Open Events. Please sign up using the mailing list to be notified of upcoming virtual open events.

There are no scheduled open events at this time.

Find out more about studying at BCI

Click below to hear from some of our former and current MSc students and get a feel for what it is like to study at the BCI.

Have you considered a career in bioinformatics?

Not all cancer research is conducted at the laboratory bench. Did you know that many cancer researchers come in the form of Bioinformaticians and Data Scientists? Click below to hear about the academic backgrounds of some of the cancer researchers at the BCI working on bioinformatics.

What is your academic background?

Before starting my PhD at the BCI, I studied physics and obtained my BSc and MSc from ETH Zurich in Switzerland. I gained my first experience with quantitative biology in higher education during my master's, where I got in touch with population dynamics and evolutionary theory. Eventually, I investigated mathematical and computational models of cancers for my master's thesis.

Why did you decide to pursue a PhD in computational biology?

To be perfectly honest, my motivation to study computational biology is the same that drove me to study physics. I enjoy developing and using mathematical and computational tools to better understand nature. However, computational biology is a relatively young discipline that is still messy in some parts compared to physics that has a long history. Following one of Steven Weinberg's four golden lessons, I go into the lesser understood field where "creative work could still be done".

Additionally, understanding the biology of cancer has the potential to improve patients' quality of life. I find it motivating to know that my research might help people in the future.

What is an average day is like for you as a computational scientist/mathematical modeller?

The focus of my group lies in mathematical and computational modelling that are then compared to genetic data. In my position, I study the resistance to cancer therapies. My day typically consists of shuffling equations back and forth, then implementing some code and thinking if my model assumptions represent the biology well. Once my model is established, I analyse data to validate my modelling outcomes.

What is your academic background?

I did my undergraduate degree at the University of Bath in Biochemistry. This included a placement year at Southampton Hospital, working in an immunotherapy focused cancer research clinical trials lab.

I then did a Masters in Applied Bioinformatics at Cranfield, which led to a PhD in Bioinformatics at Rothamsted Research, focusing on insect genomics and insecticide resistance.

Why did you decide to study/pursue a career in bioinformatics?

During my placement year at Southampton, I realised a couple of things: firstly, I didn’t particularly enjoy wet lab work; and secondly there was lots of data being produced, but no one was trained to analyse it.

After this I decided to choose a Bioinformatics-based final year project. Because I enjoyed this taster of bioinformatics, and because biochemistry jobs without wet-lab work were limited, I signed up for the Master’s degree.

My Masters fully converted me to bioinformatics. I loved the problem solving element and the freedom to work with a wide range of interesting data without having to do all the lab work!

What is an average day is like for you as a bioinformatician?

I’m currently working on patient samples for squamous cell carcinoma (SCC). I’m building a pipeline to process the sequencing data, and identify some genetic markers (e.g. mutations) which could indicate whether a patient with SCC is at a high risk of metastasis. This consists of selecting appropriate tools, writing scripts, troubleshooting the inevitable errors and analysis of the results.

What is your academic background?

I did my BSc at the University of Warwick in Biomedical Science before pursuing an MSc by research at Oxford Brookes University (project title: “Investigation Into Stress Response in Bacteria and Eukaryotes”). I then did my PhD at Oxford Brookes University, where my research looked at “The Role of Stress-Derived Vesicles in the Bystander Effect and Cancer Related Cachexia”.

Why did you decide to study/pursue a career in bioinformatics?

During my MSc and PhD I learned both Python and R and was involved in several bioinformatics based projects. I found I enjoyed the development of various tools to help with my analyses as well as working with large datasets. Bioinformatics allowed me the opportunity to pair my love of biology with my burgeoning computational abilities.

What is an average day is like for you in your current position?

My day is split into running and developing informatics pipelines on our High Performance Compute clusters and burying myself in RStudio analysing data and producing figures. I work on a variety of different projects within my research area, and have to effectively divide myself between them all. Sometimes it’s bashing my head against the wall of ancient software dependencies that don’t want to work, but these are outweighed by the highs of taking a project from just raw sequencing data through to biologically meaningful observations.

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