ReviewThe age of the “ome”: Genome, transcriptome and proteome data set collection and analysis

The current state of human genetic studies is both a marvel and a morass. A marvel in that with the completion of the human genome sequence, projects that used to take years now take months or weeks; however, this creates a wealth of data concomitant to a black hole of meaning. In terms of the well used analogy: the human genome sequence is a library in an ancient language with no Rosetta stone. Researchers have readily exploited the human genome map and thousands of candidate gene studies for a multitude of diseases have been performed. However, many of those studies have found that the variants associated with disease risk are not obvious coding changes. The question now becomes: what do these associations mean? One approach to the downstream mapping of associations is to use additional information to map which variant might truly be causative of risk and what that risk variant is doing. This review will summarize the current state of both data set collection and analysis for the understanding of DNA variants and their downstream effects on transcripts and proteins.This article is part of a Special Issue entitled 'Transcriptome'.

► eQTL analysis involves associating quantitative expression with SNP genotype. ► eQTL data is collected by microarray profiles, next generation sequencing, or mass spectrometry. ► The goal of eQTL analysis is to examine relationships between SNP alleles and expression patterns. ► Outputs from eQTL screens can be in cis or in trans. ► Network analysis involves correlation of profiles and splitting correlations into modules.