The Bioinformatics Core runs regular training courses covering a range of topics in bioinformatics and statistics. Many of these are classroom-based courses run in partnership with the University’s Bioinformatics Training Facility with an emphasis on hands-on, practical-based learning.
We also run the very popular annual CRUK Bioinformatics Summer School, offering a week-long residential training course in analysis of genomic sequencing data to CRUK-funded scientists from across the UK.
Details of our upcoming courses can be found here.
Course Index and Materials
Here is a list of courses that the Bioinformatics Core has developed or contributes to. For more information on these courses please contact Mark Fernandes.
Introductory
- An Introduction to Statistics
- Introduction to Experimental Design
- R crash course (2/3 hours introduction to R)
- R for Cancer Scientists *new*
- Introduction to R for Biologists
- Introduction to solving biological problems with Python
- Basic Unix
- Managing your Research Data
- Avoiding data disasters (Principles of Data Management and Formatting)
- Making the most of mRNA sequencing experiments at CRUK-CI
- An Introduction to Genome Browsers
- Introduction to IGV (Introducing the Integrative Genomics Viewer for visualising Next Generation Sequencing data)
Intermediate
- RNA-seq analysis in R
- Introduction to Linear Modelling with R
- Data manipulation and visualisation using R
- Data Science in Python
- Analysis of publicly available microarray data
- Introduction to High Performance Computing (HPC)
Advanced
Summer Schools (& Autumn/Winter Schools)
These are annual workshops open to all CRUK-funded researchers. Attendance on these courses is by invite only. However, the materials can be accessed via the links below
- 2020 (Jul) Functional Genomics
- 2019 (Jul) Functional Genomics
- 2018 (Jul) Functional Genomics
- 2017 (Sep) Functional Genomics
- 2017 (Jul) Analysis of Cancer Genomes
- 2016 (Dec) Essential Data Analysis Skills for Biologists
- 2016 (Jul), Analysis of Cancer Genomes
- 2015 (Jul) Best Practices in the analysis of RNA-seq and ChIP-seq data
