Workshops and courses

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An Introduction to Solving Biological Problems with R

[R](https://www.r-project.org/) is a highly-regarded, free, software environment for statistical analysis, with many useful features that promote and facilitate reproducible research. In this course, we give an introduction to the R environment and explain how it can be used to import, manipulate and analyse tabular data. After the course you should feel confident to start exploring your own dataset using the materials and references provided.

An Introduction to Solving Biological Problems with Python

This course provides a practical introduction to the writing of Python programs for the complete novice. Participants are lead through the core aspects of Python illustrated by a series of example programs. Upon completion of the course, attentive participants will be able to write simple Python programs and customize more complex code to fit their needs. Course materials are available [here](http://pycam.github.io).

Introduction to ChIP-seq Data Analysis and Visualisation using Ensembl

ChIP-seq (Chromatin ImmunoPrecipitation followed by Sequencing) is a popular high-throughput sequencing assay to identify binding sites of DNA-associated proteins and histone modifications. Determining how proteins interact with DNA and the epigenetic landscape is essential for elucidating the regulation of gene expression. The aim of this workshop is to familiarise the participants with the primary analysis of ChIP-seq data sets by providing a balanced set of lectures and practicals on analysis methodologies.

Analysis of DNA Methylation using Sequencing

This course will cover all aspects of the analysis of DNA methylation using sequencing, including primary analysis, mapping and quality control of BS-Seq data, common pitfalls and complications. It will also include exploratory analysis of methylation, looking at different methods of quantitation, and a variety of ways of looking more widely at the distribution of methylation over the genome. Finally the course will look at statistical methods to predict differential methylation.

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