mirIntegrator: Integrating miRNAs into signaling pathways

mirIntegrator is an R package for integrating microRNAs (miRNAs) into signaling pathways to perform pathway analysis using both mRNA and miRNA expressions. Typical pathway analysis methods help to investigate which pathways are relevant to a particular phenotype understudy. The input of these methods are the fold change of mRNA of two different phenotypes (e.g. control versus disease), and a set of signaling pathways. Researchers investigating miRNA cannot perform pathway analysis using traditional methods because current pathways datasets do not contain miRNA-gene interactions. mirIntegrator package aims to fill this gap by: 1. Integrating miRNAs into signaling pathways, 2. Generating a graphical representation of the augmented pathways, and 3. Facilitating the use of pathway analysis techniques when studying miRNA and mRNA expression levels. 1 Integration of miRNA into signaling pathways The main functionality of the mirIntegrator package is the integration of miRNAs into signaling pathways. The input of this functionality are a set of signaling pathways like KEGG pathways [1] or Reactome [2], and a miRNA-target interaction database like mirTarBase [3] or TargetScan [4]. The output is a set of augmented signaling pathways. Each augmented pathway contains the original sets of genes and interactions plus the set of miRNAs involved in the pathways and their miRNAtarget interactions. These interactions are the biological miRNA repression to their target genes and are represented in the model as negative interactions. Here we show an example of the method functionality. Let us say that some researchers need to integrate KEGG [1] human signaling pathways with miRNA interactions from miRTarBase [3]. Researchers must first obtain the list of pathways as a list of graph::graphNEL objects. The nodes of each pathway represent the genes involved in the pathway and the edges represent the biological interactions among those genes (activation or repression). The second step is to obtain a miRNAtarget interactions dataset as a data.frame with the columns "miRNA" and "Target.ID". Notice that the symbols used to identify the "Target.ID" column on the miRNA-target interactions dataset must be the same symbols used on the nodes of the pathways. i.e. If the genes are identified by entrezID on the pathways’ dataset, then the miRNA-targets dataset must identify the genes by entrezID as well. Once the researchers have these two datasets, they can use the function integrate_mir. To demonstrate this functionality, mirIntegrator package includes the object mirTarBase which is a copy of the experimentally validated miRNA-target interactions database miRTarBase [3]. We downloaded the miRTarBase database from http://mirtarbase.mbc.nctu.edu.tw/ on