Analyzing RNA-Seq data for differential gene expression

Materials and Software

• Data files
  • RNA-Seq data in FASTQ format (Ex: dataset1.fastq, dataset2.fastq)
• Genome sequence files
  • Genome sequence in FASTA format (Ex: REL606.fna)
  • Genome sequence gene annotations in GFF3 format (Ex: REL606.gff3)
• BWA read mapper software
  • Download BWA
  • To build, just type "make" in the source code directory.
  • To install, move the executable "bwa" to somewhere in your $PATH, like $HOME/local/bin.
  • For usage see the BWA manual.
• SAMtools
• R statistics package
  • Download and install R
• Bioconductor R modules
  • library(edgeR)
  • library(DESEQ)
  • library(Rsamtools)
• HTSeq (Python package)
  • You may need to install python (package python27 if you use MacPorts)
  • Install Numpy - (package py-numpy if you use MacPorts)
  • Install HTSeq

Commands

Align reads to reference genome

First, index your genome so BWA can map read to it:
$bwa index REL606.fna

Then, align each data set:
$bwa aln REL606.fna dataset1.fastq > datasetX.sai

And convert to SAM format (assumes single-end data): $bwa samse REL606.fna datasetX.sai datasetX.fastq > datasetX.sam

And convert to BAM format (assumes single-end data):
$samtools faidx REL606.fna
$samtools import REL606.fna datasetX.sam datasetX.unsorted.bam
$samtools sort datasetX.unsorted.bam datasetX
$samtools index datasetX.bam

Analyze differential gene expression

library(DESEQ)