The Bioinformatics Core runs regular. 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.
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 Chandu Chilamakuri.
Introductory
- An Introduction to Statistics
- Introduction to Experimental Design
- R crash course (2/3 hours introduction to R)
- R for Cancer Scientists
- 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
- Bulk RNA-seq analysis in R
- Single cell RNA-seq analysis
- 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
- Creating analysis pipelines with Nextflow
- Introduction to Docker for Bioinformatics
- Writing web-apps for Bioinformatics with Shiny
Summer Schools
From 2015 – 2012 we ran a hugely popular, annual Cancer Research UK Bioinformatics Summer School, offering a week-long residential training course in analysis of genomic sequencing data to CRUK-funded scientists from across the UK. The course materials for these and two Autumn/Winter Schools can be accessed via the links below.
- 2021 (July) Functional Genomics
- 2020 (July) Functional Genomics
- 2019 (July) Functional Genomics
- 2018 (July) Functional Genomics
- 2017 (September) Functional Genomics
- 2017 (July) Analysis of Cancer Genomes
- 2016 (December) Essential Data Analysis Skills for Biologists
- 2016 (July), Analysis of Cancer Genomes
- 2015 (July) Best Practices in the analysis of RNA-seq and ChIP-seq data
