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Analysis of RNA-seq data with Bioconductor


This course provides an introduction to the tools available through the Bioconductor project for manipulating and analysing bulk RNA-seq data. We will present a workflow for the analysis RNA-seq data starting from aligned reads in bam format and producing a list of differentially-expressed genes. We will also describe the various resources available through Bioconductor to annotate, visualise and gain biological insight from the differential expression results. 



Oscar Rueda, CRUK

Ashley Sawle, CRUK

Dr S Ballereau, CRUK

Mark Dunning, CRUK


Audience and Prerequisites

  • 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. Here is an R crash course that we suggest looking through before attending this course.
  • Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals


Syllabus, Tools and Resources

During this course you will learn about:

  • Quality assessment of raw sequencing reads and aligned reads using R
  • Sources of variation in RNA-seq data
  • Differential expression analysis using edgeR and DEseq
  • Annotation resources in Bioconductor
  • Identifying over-represented gene sets amongst a list of differentially-expressed genes


Learning Objectives

After this course you should be able to:

  • Understand the advantages and limitations of the high-throughput assays presented
  • Know what tools are available in Bioconductor for RNA-seq data analysis and understand the basic object-types that are utilised
  • Produce a list of differentially-expressed genes from an RNA-seq experiment



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