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Inferring regulatory networks from gene expression data remains a difficult task yet. Transcription factors (TFs) and microRNAs (miRNAs) are the most important dynamic regulators in the control of gene expression. Alterations in these elements have been extensively related with human malignancies including cancer. Here we present RENATO (REgulatory Network Analysis TOol), a network-based analysis web tool for the interpretation and visualization of transcriptional and post-transcriptional regulatory information. RENATO has been designed to identify common regulatory elements in a list of genes. It maps such genes to the regulatory network, extracts the corresponding regulatory connections and evaluates each regulator for significant over-representation of targets in the list. Ranked gene lists can also be analyzed with RENATO.

The implementation is supported by an inclusive database of regulatory information based mainly, on the action of transcription factors and microRNAs. Previous knowledge of the relationship between diseases and the deregulation of this elements is also included. To make these resources easily accessible, we have also implemented a set of RESTful API (Application Programming Interface) Web Services where regulatory information can be easily retrieved.

RENATO inputs a list of gene identifiers (typically, but not restricted to, differentially expressed genes in a microarray experiment). The output consists on a list of regulatory elements of the genes in the list along with the corresponding p-value of enrichment. Results are represented graphically through an interactive user-friendly web interface that relates regulatory elements to their target genes. The interface is based in modern HTML5 technologies and allows the user to explore the regulatory network found as well as executing a number of operations including different changes in the format (color, shape, size, labels, etc.), searching elements, filtering genes and performing some simple exploratory analyses that include: adding new genes to the network, including the network in the context of REACTOME, upload a file with gene attributes that can further be used for filtering purposes and uploading gene expression values that can be also used for filtering or color-labeling the genes. RENATO makes easy the exploration of regulatory networks enabling a better understanding of functional modularity and network integrity under specific perturbations.


You can get familiar with RENATO through this TUTORIAL .

Take a look also to the network viewer tutorial for specific information on the viewer.


The final aim of a typical genomic experiment is to find a molecular explanation for a given macroscopic observation. The common scenario is based on the comparison of expression patterns between different groups (i.e. control and disease, time series experiments, etc.) which results in a group genes of interest either because they co-express in a cluster or because they are significantly over- or under-expressed when two classes of experiments are compared. The deregulated list of genes can easily grow up to one hundred. Logically, one could conclude that there are not hundreds of errors (mutations, deregulation, etc.) but one common cause leading to the abnormal expression of these genes. Regulatory elements, which generally interact and regulate several genes, are features of interest because of its potential to cause this deregulated profile.

Functional analysis

To date, transcription factors (TFs) and microRNAs (miRNAs) are the best-studied and the most important dynamic regulators in the control of gene expression. Alterations in these elements have been extensively related with human malignancies including cancer. The increasing interest in identifying putative alterations in regulatory elements has lead to the development of RENATO. This tool implements two different functional analyses:

  • Single enrichment analysis: from a simple list of genes this analysis identifies enrichment of common regulators comparing the proportion in the gene list against the proportion in the background annotation.
  • Gene set analysis: this type of analyses are more sensitive than the simple enrichment analyses. Unlike the simple enrichment analysis, this method is based on a ranked list of genes to search for blocks of common regulated genes.

Single enrichment analysis

RENATO takes a list of genes of interest and extracts the regulators for each gene. Then a Fisher's exact test for 2×2 contingency tables is used to check for significant over-representation of regulatory elements in the gene list with respect to the regulation in the genome. Multiple test correction to account for the multiple hypothesis tested (one for each functional term) is applied.

Gene set analysis

Methods inspired in systems biology can use lists of genes ranked by any biological criteria (e.g. differential expression when comparing cases and healthy controls, genes with different evolutionary rates, etc.) and directly search for the distribution of blocks of functionally related genes across it without imposing any artificial threshold. Any macroscopic observation that causes this ranked list of genes will be the consequence of common regulatory activity.

Each alteration of the regulatory activity responsible for the macroscopic observation will, consequently, be found in the extremes of the ranking with highest probability. The imposition of a threshold based on the rank values which does not take into account the cooperation among genes is thus avoided under this perspective.

Technical information