Analysis of RNA-Seq data with TopHat and Cufflinks for genome-wide expression analysis of jasmonate-treated plants and plant cultures

Abstract

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.