The recent development of various deep sequencing techniques has led to the most 
powerful transcript profiling method available to date, RNA sequencing or 
RNA-Seq. Besides the identification of new genes and new splice variants of 
known genes, RNA-Seq allows to compare the whole transcriptome of any organism 
under two or more experimental conditions, such as before and after jasmonate 
treatment. However, the vast amounts of data generated during RNA-Seq 
experiments require complex computational methods for read mapping and 
expression quantification. Here, we describe a detailed protocol for the 
analysis of deep sequencing data, starting from the raw RNA-Seq reads. First, a 
quality check is performed on the raw reads to assess the quality of the 
sequencing. Subsequently, adapters and low-quality sequences are trimmed off the 
raw reads. The resulting processed reads are mapped to the reference genome, and 
the mapped reads are counted to generate expression data for the annotated genes 
for each sample. This method can be used for the analysis of RNA-Seq data of any 
organism for which a reference genome is available.