Introduction to bioinformatics for Cancer Research

The course aim is to teach basic statistical analysis of a range of types of data that are increasingly common in biological and clinical research, including gene expression microarrays, RNA-seq, SNP arrays, DNA methylation microarrays, ChIP-seq, siRNA screens and proteomics arrays. Particular focus will be placed on the interpretation of results.

We will teach the theoretical basis of such work, along with research examples to demonstrate how data analysis can help to provide publishable insights into cancer biology and its clinical implications. Detailed practical guides, including worked examples, will be provided to assist with application of this knowledge after the course – the examples are structured so as to be adaptable, and thus as a result of this course the attendees will be able to perform a range of data analyses themselves in their own research environment.

Course material will be primarily delivered through a series of lectures, teaching required theory and presenting examples of real applications of the techniques covered. Online resources will be available to course attendees, including links to datasets and tools used in the examples, instructions for installing any software used in the lectures, and commands to copy in order to reproduce examples.

This course will be of interest to clinical and biological scientists and postdocs who are undertaking research projects or pursuing research careers.

Attendees will leave the course with an understanding of how to leverage large biological and clinical datasets in order to gain insight into molecular biology relevant to their area of research. This will enable more scientists and clinicians to undertake research investigations without the investments of time and resource required for laboratory work, and so should accelerate the process of increasing our understanding of the biological mechanisms of disease and how this can be applied in a clinical setting.