Professor Richard Houlston’s team works to identify cancer susceptibility genes and understand how these can cause cancer.
Professor Richard Houlston is Head of the Division of Genetics and Epidemiology. His research focuses on the identification and characterisation of genetic susceptibility to cancer.
I joined the team in 2019 as a PhD Student. My research aim is to identify and characterise non-coding drivers for colorectal cancer.
I am very interested in the biological mechanisms that are underlying human diseases and really keen on elucidating these mechanisms using high throughput data and computational methods. My current routine work is carrying out complex custom computational analysis and creating bioinformatics pipelines using my scientific programming skills in R and Python. I like developing dynamic web applications using Rshiny and Flask web frameworks, and relational databases.
I come from a PhD background in applied statistics, with knowledge of both frequentist and Bayesian statistics, and our group is using a variety of methods to understand how the incidence of colorectal cancer is affected by both genetic and environmental variables. In particular, I hope to improve my skills in bioinformatics and systems biology within the application of cancer.
I have worked at the ICR since 2016, providing PA support at senior executive level within the Division of Genetics and Epidemiology. I assist the Head of Division and teams to communicate, collaborate and maintain working relationships while focusing on the identification and characterisation of genetic susceptibility to cancer.
I am a Bioinformatician, currently working on evaluating genetic pre-disposition factors and environmental risk factors of multiple cancer types.
Professor Clare Turnbull uses genetic sequencing technologies to identify and characterise genetic predispositions to testicular, breast and ovarian cancers.
The team are investigating ways to optimise ‘next-generation’ sequencing technologies and analyses of these data in order to identify novel cancer predisposition genes.