At least four individuals per species were investigated, with two to three times more males sampled than females. Before discussing the advantages of these techniques, it is

important also to recognize their limitations. Both approaches are restricted to polyadenylated RNAs and to protein-coding mRNAs and could not fully explore the relative levels of alternatively spliced transcripts. The DGE method also required 4,869 genes to be discarded since these are without a site for the DpnII restriction enzyme for any of the three species. Finally, it needs to be recognized that levels of transcripts and proteins tend to be only modestly correlated, if at all ( Ghazalpour et al., 2011), and thus that conclusions based on transcript abundance may not be translated to the protein level. The DGE approach employed two to three million 20 bp tags from the 3′ of transcripts per sample that were mapped to gene models DAPT ic50 within reference genome assemblies. Not surprisingly, perhaps, the DGE method, which is based on the Illumina GAIIx this website next-generation sequencing technology, outperforms two microarray technologies, capturing more genes, more differentially expressed genes, and more conserved modules (defined below).

Konopka et al. (2012) thus concentrate on the DGE results. Babbitt et al. (2010) previously applied DGE to ADAMTS5 frontal cortex samples from three humans, three chimpanzees, and three macaques, and their lists of genes that were differentially expressed between human and chimpanzee are similar to those identified by Konopka et al. (2012). The first major findings of Konopka et al. (2012) are that genes that are differentially expressed in human, with respect to the other two species, are more numerous for the frontal pole than they are for the other two brain regions and that this bias is not observed for the frontal pole samples of chimpanzee or macaque. In the human frontal pole samples, 1,450 genes are differentially expressed, and Konopka et al. (2012) make mention of 23 that contribute to

a variety of neurobiological processes, such as neuron maturation and neurotrophin signaling. Further advances from this study arose from Konopka et al. (2012)’s analyses of gene coexpression networks derived from the three brain regions of the three primates. These networks are constructed from genes whose expression levels are correlated (either positively or negatively) among these samples, with genes that are more highly correlated being more closely neighboring in the network (Oldham et al., 2006). In these experiments, each gene expression level is the sum of its transcripts’ abundances in all cells of each sample. There are two explanations for why two gene expression levels may be positively correlated across samples.