Nucleic Acids Research, 2011, Vol. 39, Database issue D161 Experiments miRNAs (Proﬁles) mRNAs Diseases Tissues Perturbations Data source Table 1. Summary statistics of data sets in miRGator v2.0 Data category miRNA set Gene set miRNA proﬁles Data type Description Disease- Disease associated miRNAs miRNAs DEmiRs DEmiRs under diseases/ perturbations miRNA Predicted miRNA targets target genes miRNA- miRNA knockout perturbed miRNA DEGs overexpression Coexpressed Positive and genes negative coexpression between miRNA and mRNA from paired expression proﬁles Expression miRNA expression proﬁles proﬁles under various conditions Phenotype Proﬁles of disease proﬁles associations for each miRNA 146 (8013) 5 (92) 20 (205) 22 (2538) – – – 146 (8013) 689 354 (up) 340 (down) 689 700 – – 685 354 (up) 340 (down) 19 069 15 708 20 887 27 830 – – – – 59 47 – 4 12 11 47 59 25 21 – 4 12 13 21 25 – 128 – 16 27 – 128 – PhenomiR GEO ArrayExpress miBridge, TargetScan, PITA, miRanda GEO GEO GEO GEO ArrayExpress PhenomiR the list of all miRNAs, click to show the list of miRNA proﬁles containing the selected miRNA, and then choose a speciﬁc miRNA experiment, where the actual expression values are displayed as a heatmap. MiRNA–disease asso- ciation, and tissue-speciﬁc or disease related expression data can be accessed in a similar manner. Particularly, miRGator v2.0 is useful to obtain integrated information on miRNA target reliability. The miRNA–target relationship may be supported by multiple, independent evidences such as target prediction, up-regulation on miRNA knockout, down-regulation on miRNA overexpression and miRNA–mRNA anti- coexpression from paired expression experiments. These data supporting the miRNA–target relation are shown side-by-side simultaneously. For example, RAC1 (Entrez Gene ID: 5879) is down-regulated by overexpression of hsa-mir-155 in the GSE14477 data set. RAC1 is predicted as a target by two methods (PITA and miRanda) and also shows a strong negative correlation in two out of the 22 miRNA–mRNA paired expression data sets. This strongly suggests that RAC1 is a genuine target of hsa-mir-155, which needs further experimental validation. ASSOCIATION ANALYSIS AND ASSOCIATION NETWORK VIEWER The data sets in miRGator belong to one of the three categories of gene set, miRNA set and miRNA-related proﬁle. Within the data types of the same category, asso- ciation relationships are established by the overlap among the gene/miRNA sets or by the similarity of proﬁles (Figure 3). For example, miRNA-perturbed DEGs can be linked to miRNA targets by GSA. Similarly, differen- tially expressed miRNAs (DEmiRs) can be associated to disease-related miRNAs. In miRNA proﬁle analysis (miRPA), a pair of miRNAs may be functionally related if their expression is regulated in parallel in various experi- mental and disease conditions as is the case with a pair of genes. Likewise, a pair of miRNAs with similar phenotyp- ic or disease-related patterns is likely to be functionally associated. The resulting associations can be visualized by the association network viewer (bottom right in Figure 1). The network viewer is equipped with useful features such as save network, export image, highlighting nodes interactively, group nodes by disease, etc. and a detailed description is provided in the on-line documentation. miRSA AND GSA The GSA/miRSA module allows the user to enter a list of genes and perform GSA/miRSA for statistical enrichment. Association between two sets of miRNAs is also possible in a similar manner to GSA, which we call miRSA. Most representative gene IDs are cross-referenced and automat- ically converted to the miRBase ID or NCBI entrez gene IDs. For miRSA, we use our own compilation of miRNA sets (i.e. disease-associated miRNAs and DEmiRs; data type A and B in Figure 2) in addition to the PhenomiR database. The GSA is applied for the predicted miRNA targets (data type C), miRNA-perturbed DEGs (data type D), anti-coexpressed genes (data type E). Additional categories include the GO, disease gene [Online Mendelian Inheritance in Man (OMIM, 18) and genetic association database (GAD, 19)], and pathway (KEGG and BioCarta) analyses. The user may enter a list of genes or miRNAs to check any relevance to miRNA biology or existing annotations. On log-in, the