Recent developments in bioinformatics and sequencing techniques have allowed analyses to shift from individual genes to networks of co-expressed genes, thus providing a more integrated, biologically relevant view of AUD. Application of novel bioinformatics techniques, such as weighted gene co-expression network analysis (WGCNA), has uncovered gene co-expression networks associated with alcohol dependence (Ponomarev et al., 2012). Furthermore, in the last five years, RNA-seq has become an invaluable tool to analyze the transcriptome. Due to advances in sequencing technology, RNA-seq makes it possible to identify exons and introns by the boundaries of their genes, to identify transcription initiation start sites and splicing variants, and to quantify exon and splicing isoform expression (Farris and Mayfield, 2014). This allows the study of alcohol-induced alterations in gene expression in the human brain at an unprecedented level, and the advancement of potentially relevant treatment strategies for normalizing addiction-related changes in gene expression. With these rapid advances also come challenges that are further amplified when considering the broad range of ‘omic’ approaches and modeling requirements for understanding complex disease (Coveney et al., 2016).
