Analysis of High-throughput sequencing data with Bioconductor

Prerequisites

This course provides an introduction to the tools available through the Bioconductor project for manipulating and analysing high-throughput sequencing (HTS) data. We will present workflows for the analysis of ChIP-Seq and RNA-seq data starting from aligned reads in bam format. We will also describe the various resources available through Bioconductor to annotate and visualize HTS data, which can be applied to any type of sequencing experiment.

During this course you will learn about:

• Quality assessment of raw sequencing reads and aligned reads using R.
• Differential expression analysis using edgeR and DEseq.
• Annotating HTS results with Bioconductor.
• Importing ChIP-Seq peaks and performing downstream analysis.
• Integrating ChIP-Seq and RNA-Seq data.

The course timetable is available here.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book by linking here.

• Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals
• Further details regarding eligibility criteria are available here
• Further details regarding the charging policy are available here

• A knowledge of current sequencing technologies, data formats (e.g. fastq and bam) and alignment
• A very basic knowledge of UNIX would be an advantage, but nothing will be assumed and extremely little will be required
• Attendees should be comfortable with using the R statistical language to read and manipulate data, and produce simple graphs

Number of sessions: 3

• Know what tools are available in Bioconductor for HTS analysis and understand the basic object-types that are utilised.
• Given a set of gene identifiers, find out whereabouts in the genome they are located, and vice-versa (i.e. go from genomic coordinates to genes).
• Produce a list of differentially expressed genes from an RNA-Seq experiment.
• Import a set of ChIP-Seq peaks and investigate their biological context.

• To provide an understanding of how aligned sequencing reads, genome sequences and genomic regions are represented in R.
• To encourage confidence in reading sequencing reads into R, performing quality assessment and executing standard pipelines for RNA-Seq and ChIP-Seq analysis

Presentations and practicals

3

A number of times per year

• An Introduction to Solving Biological Problems with R
• R object-oriented programming and package development
• CRUK: Analysis of publicly available microarray data
• Next Generation Sequencing data analysis
• Experimental Design (ONLINE LIVE TRAINING)
• Analysis of mapped NGS data with SeqMonk (ONLINE LIVE TRAINING)