Once an acceptable normalization method is determined, summarized read counts can be evaluated for divergent expression profiles between two or more conditions. RNA-Seq is a powerful tool for the detection of differentially expressed features, capable of capturing weakly expressed genes and alternatively splices transcripts within a single experiment (Bottomly et al., 2011; Marioni, Mason, Mane, Stephens, & Gilad, 2008). Although it is challenging to use short-read sequencers to quantify splice variants having identical exons, several algorithms exist for computing the expression of full-length isoforms (Garber, Grabherr, Guttman, & Trapnell, 2011; Trapnell et al., 2012; Xing et al., 2006). Recognition of alternatively spliced transcripts, and their individual exons, is an important aspect for interpreting the neurobiology of disease. The human transcriptome is able to generate a tremendous degree of biodiversity, with ~95% of all multiexon genes undergoing alternative splicing (Pan, Kaiguo, Razak, Westwood, & Gerlai, 2011). Humans, and closely related primates, exhibit the greatest degree of complexity in splicing, with the human brain being the most diverse among several tissue types (Barbosa-Morais et al., 2012). The higher rate of alternative splicing in human brain may underscore evolutionary remodeling for higher cognitive function while generating greater susceptibility to neuropsychiatric diseases.
