Recent advances in next-generation sequencing technology allow high-throughput RNA sequencing (RNA-Seq) to be widely applied in transcriptomic studies. For model organisms with a reference genome, the first step in analysis of RNA-Seq data involves mapping of short-read sequences to the reference genome. Reference-guided transcriptome assembly is an optional step, which is recommended if the aim is to identify novel transcripts. Following read mapping, the primary interest of biologists in many RNA-Seq studies is the investigation of differential expression between experimental groups. In this review, we discuss recent developments in RNA-Seq data analysis applied to model organisms, including methods and algorithms for direct mapping, reference-guided transcriptome assembly and differential expression analysis, and provide insights for the future direction of RNA-Seq.

RNA-Seq is a revolutionary methodology that employs high-throughput sequencing technologies to enable highly sensitive detection and quantification of RNA in biological samples. Mapping of RNA-Seq data to a reference is a fundamental step for all forms of RNA-Seq data analysis in model organisms, and differential expression analysis is the primary interest of biologists in many RNA-Seq studies. In this review we discuss recent developments in these two fields and provide insights for the future direction of RNA-Seq. We see our review as a resource for the community that will enable researchers to select the most appropriate tools for RNA-Seq data analysis.