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This tutorial will illustrate how to use standalone tools, together with R and Bioconductor for the analysis of RNA-seq data. Keep in mind that this is a rapidly evolving field and that this document is not intended as a review of the many tools available to perform each step; instead, we will cover one of the many existing workflows to analyse this type of data.

We will be working with a subset of a publicly available dataset fromDrosophila melanogaster, which is available both in the Short Read archive (SRP001537 - raw data) and in Bioconductor (pasilla package - processed data). For more information about this dataset please refer to the original publication (Brooks et al. 2010).

The tools and R packages that we will be using during the practical are listed below (seeSoftware requirements) and the necessary data files can be foundhere. After dowloading and uncompressing thetar.gz file, you should have the following directory structure in your computer:

RNAseq|-- reference               # reference info (e.g. genome sequence and annotation)`-- data    |-- raw                 # raw data: fastq files    |-- mapped              # mapped data: BAM files    `-- demultiplexing      # extra fastq files for the demultiplexing section

This work is licensed under aCreative Commons Attribution-ShareAlike 3.0 Unported License. This means that you are able to copy, share and modify the work, as long as the result is distributed under the same license.

1. Dealing with raw data
   1. The FASTQ format
   2. Quality assessment (QA)
   3. Filtering FASTQ files
   4. De-multiplexing samples
   5. Aligning reads to the genome
2. Dealing with aligned data
   1. The SAM/BAM format
   2. Visualising aligned reads
   3. Filtering BAM files
   4. Gene-centric analyses:
      1. Counting reads overlapping annotated genes
         • With htseq-count
         • With R
         • Alternative approaches
      2. Normalising counts
         • With RPKMs
         • With DESeq2
      3. Differential gene expression
   5. Exon-centric analyses:
      1. Differential exon usage
   6. Transcript-centric analyses:
      1. Identification, annotation and visualisation of splicing switch events

Note: depending on the topics covered in the course some of these tools might not be used.

• Standalone tools:

  • FastQC
  • PRINSEQ
  • eautils
  • samtools
  • IGV
  • htseq-count

• Bioconductor packages:

  • GenomicRanges
  • GenomicAlignments
  • Rsamtools
  • biomaRt
  • pasilla
  • DESeq2
  • DEXSeq (only for some dependencies)

• Complete course data, including command outputs and R sessions

• R reference card
• Unix comand line cheat sheet
• R and Bioconductor tutorial by Thomas Girke
• Do not forget to check the documentation for the packages used in the practical!

• Introduction to my thesis
• Online training resources at the EBI website
• Bioinformatic methods I - Coursera
• Bioinformatic methods II - Coursera
• Data analysis for genomics
• Courses from the Bioconductor website

This tutorial has been inspired on material developed by Ângela Gonçalves, Nicolas Delhomme, Simon Anders and Martin Morgan, who I would like to thank and acknowledge. Special thanks must go to Ângela Gonçalves and Mitra Barzine, with whom I have been teaching, and to Gabriella Rustici, for always finding a way to organise a new course.