RNA-Seq offers several advancements that make it particularly valuable for studies of global gene expression. By excluding cloning steps and related sequence errors, RNA-Seq produces more accurate data on transcript sequences and requires less RNA than microarray techniques (Z. Wang, Gerstein, & Snyder, 2009). Unlike hybridization-based technologies, RNA-Seq is not limited to detecting transcripts that correspond to predefined sets of well-annotated genes, and it allows the detection of alternative splice variants, the alternatively transcribed versions of a single gene resulting in different mRNAs that, in turn, are translated into different protein isoforms, so that a single gene may code for multiple proteins. RNA-Seq also allows the detection of novel transcripts (Mortazavi, Williams, McCue, Schaeffer, & Wold, 2008; E. T. Wang, et al., 2008). This makes RNA-Seq especially useful for studying complex transcriptomes. Moreover, RNA-Seq quantifies expression levels with high accuracy (Asmann, et al., 2009). The absence of significant background signal, more relaxed limits for quantification, and the greater range of expression levels that might be detected compared to microarrays allow the detection of genes expressed either at very low or
