ReNE symbol list. For each symbol, the corresponding mapping rule for KEGG and Reactome is also provided.

<p>Some nodes and edges, mapped as "n/a", are not present in publicly available pathways and are generated by ReNE. * and ** correspond to node/edge definitions that are not informative for the plugin purpose. Such entities are mapped maintaining the original definition when possible, otherwise they are labeled as "Undefined".</p

ReNE: A Cytoscape Plugin for Regulatory Network Enhancement

<div><p>One of the biggest challenges in the study of biological regulatory mechanisms is the integration, americanmodeling, and analysis of the complex interactions which take place in biological networks. Despite post transcriptional regulatory elements (i.e., miRNAs) are widely investigated in current research, their usage and visualization in biological networks is very limited. Regulatory networks are commonly limited to gene entities. To integrate networks with post transcriptional regulatory data, researchers are therefore forced to manually resort to specific third party databases. In this context, we introduce ReNE, a Cytoscape 3.x plugin designed to automatically enrich a standard gene-based regulatory network with more detailed transcriptional, post transcriptional, and translational data, resulting in an enhanced network that more precisely models the actual biological regulatory mechanisms. ReNE can automatically import a network layout from the Reactome or KEGG repositories, or work with custom pathways described using a standard OWL/XML data format that the Cytoscape import procedure accepts. Moreover, ReNE allows researchers to merge multiple pathways coming from different sources. The merged network structure is normalized to guarantee a consistent and uniform description of the network nodes and edges and to enrich all integrated data with additional annotations retrieved from genome-wide databases like NCBI, thus producing a pathway fully manageable through the Cytoscape environment. The normalized network is then analyzed to include missing transcription factors, miRNAs, and proteins. The resulting enhanced network is still a fully functional Cytoscape network where each regulatory element (transcription factor, miRNA, gene, protein) and regulatory mechanism (up-regulation/down-regulation) is clearly visually identifiable, thus enabling a better visual understanding of its role and the effect in the network behavior. The enhanced network produced by ReNE is exportable in multiple formats for further analysis via third party applications. ReNE can be freely installed from the Cytoscape App Store (<a href="http://apps.cytoscape.org/apps/rene" target="_blank">http://apps.cytoscape.org/apps/rene</a>) and the full source code is freely available for download through a SVN repository accessible at <a href="http://www.sysbio.polito.it/tools_svn/BioInformatics/Rene/releases/" target="_blank">http://www.sysbio.polito.it/tools_svn/BioInformatics/Rene/releases/</a>. ReNE enhances a network by only integrating data from public repositories, without any inference or prediction. The reliability of the introduced interactions only depends on the reliability of the source data, which is out of control of ReNe developers.</p></div

The image shows the Parkinson Disease network obtained from KEGG (ID: hsa05012), after its complete enhancing and layouting.

<p>(1) The entire enhanced network. (2) Zoomed section highlighting the role of <i>HNF4A</i> as mediator of <i>miR-3646</i> acting as hub for the inner regulation of the pathway.</p

The image shows the set of enhancing steps automatically performed by ReNE.

<p>(1) The original regulatory network. (2) Transcriptional regulation enhancement: Transcription Factors coding genes are added to the network. (3) Translation regulation enhancement: genes are normalized in terms of symbols and accessions retrieving information from NCBI, while the network is enhanced with the protein coded by the genes retrieving information from Uniprot. (4) Post-transcriptional regulation enhancement: each gene is analyzed for discovering possible co-expressed intragenic miRNAs. For each miRNA, the list of its target genes is intersected with the network entities, and for each match the network is enhanced with a regulatory edge.</p

P-value of the chi square test (N = patients).

<p>P-value of the chi square test (N = patients).</p

Number of patients sampled in the care pathway through health facilities.

<p>T1: Hospital admission T2: Hospital discharge T3: Admission into local care setting T4: Discharge from local care setting.</p

Additional file 2 of CyTRANSFINDER: a Cytoscape 3.3 plugin for three-component (TF, gene, miRNA) signal transduction pathway construction

∙ srl.txt: the file containing the SRL. ∙ dgl.txt: the file containing the DGL. ∙ Indirect-miRNA-STPP.xlsx: an excel file containing the full list of 312 Indirect miRNA STPPs identified by CyTRANSFINDER using human miR-146a as the source intergenic miRNA and miR-146a 223 target genes according to TargetScan 5.2 as DGL. Results are computed using miRNA targets confirmed in at lease one source database. SmiRNA: source intragenic miRNA; TF: Transcription Factor; DG: Destination Gene. an excel file containing the full list of 292 Double Indirect miRNA STPPs identified by TransFINDER using DNM3 as the Source Gene, the cognate human intragenic miR-214 as source intragenic miRNA (SmiRNA) and a previously described signature of 73 genes whose expression was driven by miR-214 [33] as destination genes (DG). TF: Transcription Factor; TF target (Gene): Transcription Factor target gene, which is also the host gene for a miRNA; intragenic miRNA: miRNA located inside the TF target gene; DG: Destination Gene, list of targets of the intragenic miRNAs predicted by at least two algorithms TS: Transcription Factor. (ZIP 192 kb

Patients’ characteristics (N = patients).

<p>Patients’ characteristics (N = patients).</p

Population characteristics.

<p>Population characteristics.</p