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Gene ontology gene sets For our analysis, we obtained the Gene Ontology annotation for entrez gene IDs ABT888 from Bioconductor annotation packages org. Hs. eg. db and GO. db. Gene family gene sets We retrieved gene family protein tag information and entrez identifiers of the genes in the HGNC database. We defined gene family gene sets for groups of genes that shared the same HGNC protein family tag. From the HGNC database we gathered a total of 587 gene family gene sets comprising a total of 16, 722 entrez genes. Gene sets of co localized adjacent genes For the identification of genomic regions with enriched subnetworks of interacting genes we defined gene sets of genes that were adjacently located within a chromosomal region from overlaping sliding windows along the human chromosomes.<br><br> We defined gene sets from 1M b sliding windows along the human chromosomes with a 500Kb overlap between adjacent windows. The gene sets of co located genes AEB071 価格 were defined for chromosome regions of 1M b with 500Kb overlap to mimic the extend for co expressed gene clusters. Gene pair enrichment analysis The GPEA facilitates the identification and ranking of significant subnetworks of defined gene sets for a given network. For p genes there is a total of N p 2 different gene pairs. If there are pS genes for a particular gene set then the total number of gene pairs for this gene set is mS pS 2. When a network G contains n interactions, of which k interactions are among genes from the given gene set S, then a p value for the enrichment of gene pairs of this gene set S can be calculated from the following hypergeometric distribution This p value gives an estimate for the probability to observe k or more interactions between genes from a given gene set S.<br><br> We performed a AG-014699 ic50 GPEA analysis for the inferred GRNs for ∼ 8, 000 gene sets of GO biological process, ∼ 500 gene sets of gene families and ∼ 4, 000 gene sets of co located genes. For the analysis the inferred networks are expected to show a strong association to gene sets of a biological functional and spatial context. Therefore, we considered a more stringent significance level of 0. 001 relative to the number of performed test in the range of 103. Further, we considered a Bonferroni multiple testing correction. Network centrality measures For the network analysis we measured the degree centrality and edge density.<br><br> The degree centrality was defined as the total number of direct neighbors of a gene gi of an undirected gene regulatory network. The edge density of a network was the number of edges divided by the maximal number of possible edges. For an undirected network this number was given by n 2, whereas n is the total number of genes. Protein interaction databases We gathered and processed interactions from Biogrid, Intact, Mint, Hprd, String. Further, we considered the largest manually curated human signaling network which we denote in the text as SingNet, a pathway protein interaction network extracted from the bioconductor package graphite and the integrative network from ConsensusPathDB. We assigned their entrez gene identifiers mapping when available from the interaction database or converted the identifiers to entrez identifiers using the annotation from the bioconductor package org.