The use of the following workflows is highly suggested, since it provides optimized use of the DIANA-web server algorithms. These pipelines can be utilized to analyze user data derived from small scale and high throughput experiments directly from the DIANA-microT web server interface, without the necessity to install or implement any kind of software.
In all workflows the user has to specify the species and two lists of differentially expressed mRNAs (microarray/RNA-Seq) and miRNAs (microarray/sRNA-Seq) respectively. The gene list has to contain Ensembl gene identifiers, while the miRNA list should be composed of miRNA names/identifiers according to miRBase nomenclature. miRNA and gene identifiers can optionally be followed by fold change values . In this case, the workflows automatically matches suppressed genes are with overexpressed miRNAs (and vice versa).
Species:
Human
Mouse
D. melanogaster
C. elegans
Workflows:
1) Enrichment analysis of validated miRNA:gene interactions followed by targeted Pathway analysis
The implemented workflow initially performs enrichment analysis in experimentally validated targets derived from DIANA-TarBase v6.0 , identifying miRNAs significantly controlling the set(s) of differentially expressed genes. Subsequently a miRNA-targeted pathway analysis is implemented with DIANA-miRPath v2 .
2) Enrichment analysis of predicted miRNA:gene interactions followed by targeted Pathway analysis
The implemented workflow initially performs enrichment analysis in in-silico predicted targets derived from DIANA-microT-CDS , identifying miRNAs significantly controlling the set(s) of differentially expressed genes. Subsequently a miRNA-targeted pathway analysis is implemented with DIANA-miRPath v2 .
3) Optimized enrichment analysis of predicted miRNA:gene interactions followed by targeted Pathway analysis
Interactions between user defined miRNA and gene sets are in silico identified using DIANA-microT-CDS . A subsequent miRNA target enrichment analysis identifies miRNAs controlling significantly the sets of differentially expressed genes. The pipeline is automatically repeated for different prediction thresholds (from sensitive to more stringent), in order to minimize the effect of the selected settings to the result. By utilizing meta-analysis statistics, the server combines the p-values from each repetition into a total p-value for each miRNA, signifying its effect on the selected genes for all utilized thresholds. In the last step of the pipeline, the identified miRNAs are subjected to a functional analysis with DIANA-miRPath v2 , where pathways controlled by the combined action of these miRNAs are detected.
4) "Personalized" selection of miRNA-specific validated/predicted interactions, followed by miRNA Pathway analysis
In this workflow, the algorithm "personalizes" the target identification module for each miRNA. It initially identifies the number of available interactions in DIANA-TarBase and DIANA-microT-CDS (validated vs predicted) and automatically selects to use validated targets only in the cases of well-annotated miRNAs. Computationally identified interactions are used otherwise. In the last step of the pipeline, the miRNAs with targets predicted form DIANA-microT-CDS or experimentally verified targets from TarBase v6 are subjected to a functional analysis with DIANA-miRPath v2 , where pathways controlled by the combined action of these miRNAs are detected.
