No single procedure has yet emerged as a gold standard for RNA-Seq analyses. Differing methodologies for profiling expression can reveal discrepant findings in the identification of differentially expressed genes from the same experimental dataset (Rapaport et al., 2013; Soneson & Delorenzi, 2013; Tarazona, García-Alcalde, Dopazo, Ferrer, & Conesa, 2011). In order to adequately manage bioinformatics pipelines, multiple in silico experimental designs, rather than a one size fits all approach, may initially need to be explored before selecting a suitable model of normalization. RNA-Seq expression data from the prefrontal cortex are illustrated using different representative methods of normalization. The intersample correlations among controls and alcoholics fluctuate according to the normalization strategy and impact the extent of within group variation (Fig. 11.7). RPKM values have the highest proportion of variability, which may impede the identification of differentially expressed features between alcoholics and controls. A comprehensive evaluation of normalization techniques for RNA-Seq data has previously suggested the RPKM approach is ineffective and should cease to be used for evaluating differential expression (Dillies et al., 2013). Additionally, RPKM data may fail to adequately account
