The bench scientist’s guide to statistical analysis of RNA-seq data

As a method for characterizing global changes in transcription, RNA-Seq is an attractive option because of the ability to quantify differences in mRNA abundance in response to various treatments and diseases, as well as to detect alternative splice variants and novel transcripts [1]. Compared to microarray techniques, RNA-Seq eliminates the need for prior speciesspecific sequence information and overcomes the limitation of detecting low abundance transcripts. In addition, early studies have demonstrated that RNA-Seq is very reliable in terms of technical reproducibility [2]. As a result, biologists studying an array of model and non-model organisms are beginning to utilize RNA-Seq analysis with ever growing frequency [3-7]. However, without experience using bioinformatics methods, the large number of choices available to analyze differential expression can be overwhelming for the bench scientist (see Table one in [8]).