Analysis of distant regulatory elements of coregulated genes (DiRE, http://dire.dcode.org) [ 31] was performed using a random set of 5000 background genes and using target elements of top 3 evolutionary conserved regions (ECRs) and promoter ECRs.
Median normalization of raw expression data and identification of differentially expressed genes using a random variance t-test was performed using BRB-ArrayTools [ 39] version 4.1.0 Beta 2 Release (developed by Dr. Richard Simon and BRBArrayTools Development Team members).
EnrichNet (Glaab et al., 2012) maps the input gene set onto a molecular interaction network and, using a random walk, scores distances between the genes and pathways/processes in a reference database.
Exercise response genes were evaluated using a random variance t test in a paired, class comparison analysis of control subjects before and after exercise, and 21 genes were identified as being differentially expressed (Table 2).
Differentially expressed genes were identified using a random variance ANOVA test.
The role of novel (non-disease genes) is assessed by identifying functionally related genes using a random-walk-with-restart algorithm over a gene interaction network.
All tests involving gene expression data used a random variance model [ 38].
Two additional groups were included in the analysis: Random genes: two groups of 20 and 30 genes were randomly selected from the A. gambiae and D. melanogaster genomes, respectively, using the "Random Gene Selection" tool of RSA-Tools [ 65].
To ensure the selection of statistically relevant motifs, for each condition tested (Dyad, 5, 6, 7 or 8 words length), three different random gene groups of the same size were assessed in parallel (using the "random gene selection" algorithm).
Although we tried to remove non-specific topics by using the random gene set and appropriate background set, we sometimes see very general topics found that are not specific or with enough granularity to the studied genes.
