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.
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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.
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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.

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Intermediate

• RNA-seq analysis in R
• Introduction to Linear Modelling with R

• Data manipulation and visualisation using R
• Analysis of publicly available microarray data
• Introduction to High Performance Computing (HPC)
• Python functions and modules: best practices

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Advanced

• Introduction to Docker for Bioinformatics
• Writing web-apps for Bioinformatics with Shiny

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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

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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
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• 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
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