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 2023
- Deadline for postgraduate Overseas applications: 01 September 2023
- Deadline for postgraduate DL applications and postgraduate Home applications: 08 September 2023
For more information and to apply, visit the Queen Mary University of London website:
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.
Programme Team
The programme is being developed and delivered by our specialists in the field
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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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