The training that we offer (and where to go for courses that we currently don't)
For a list of current and upcoming courses, please see 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
- Basic Unix
- An Introduction to Genome Browsers
- Introduction to IGV (Introducing the Integrative Genomics Viewer for visualising Next Generation Sequencing data)
- Introduction to Galaxy: data manipulation and visualisation
- Introduction to solving biological problems with Python
- R crash course (2/3 hours introduction to R)
- Introduction to solving biological problems with R
- Avoiding data disasters (Principles of Data Management and Formatting)
- Beginners guide to version control with git
- Making the most of mRNA sequencing experiments at CRUK C.I.
--------------------------
Intermediate
- Data manipulation and visualisation using R
- Analysis of publicly available microarray data
- Introduction to High Performance Computing (HPC)
- Python functions and modules: best practices
--------------------------
Advanced
--------------------------
Summer / 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
- 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, Best Practices in the analysis of RNA-seq and ChIP-seq data
--------------------------
Retired
- Analysis of high-throughput sequencing data with Bioconductor
- Introduction to Statistical Analysis using R Commander (link)
A version of our Introductory Statistics course that used the R Commander plugin to do the practical
--------------------------
- Microarray Data Analysis using R and Bioconductor (link)
A course on microarrays that references many technologies now considered obsolete, and concentrates on pre-processing the raw data. For a more modern perspective on microarrays, including download data from public repositories, see our new course
--------------------------