miRmapper: A Tool for Interpretation of miRNA–mRNA Interaction Networks

Abstract

It is estimated that 30% of all genes in the mammalian cells are regulated by microRNA (miRNAs). The most relevant miRNAs in a cellular context are not necessarily those with the greatest change in expression levels between healthy and diseased tissue. Differentially expressed (DE) miRNAs that modulate a large number of messenger RNA (mRNA) transcripts ultimately have a greater influence in determining phenotypic outcomes and are more important in a global biological context than miRNAs that modulate just a few mRNA transcripts. Here, we describe the development of a tool, “miRmapper”, which identifies the most dominant miRNAs in a miRNA–mRNA network and recognizes similarities between miRNAs based on commonly regulated mRNAs. Using a list of miRNA–target gene interactions and a list of DE transcripts, miRmapper provides several outputs: (1) an adjacency matrix that is used to calculate miRNA similarity utilizing the Jaccard distance; (2) a dendrogram and (3) an identity heatmap displaying miRNA clusters based on their effect on mRNA expression; (4) a miRNA impact table and (5) a barplot that provides a visual illustration of this impact. We tested this tool using nonmetastatic and metastatic bladder cancer cell lines and demonstrated that the most relevant miRNAs in a cellular context are not necessarily those with the greatest fold change. Additionally, by exploiting the Jaccard distance, we unraveled novel cooperative interactions between miRNAs from independent families in regulating common target mRNAs; i.e., five of the top 10 miRNAs act in synergy.

Computational Network Analysis Identifies Evolutionarily Conserved miRNA Gene Interactions Potentially Regulating Immune Response in Bovine Trypanosomosis

“…In this study, we identified and elucidated conserved miRNAs from a list of gene targets important in innate immune response during bovine trypanosomosis through computational prediction analysis. Previous studies have focused on the 3′UTR region to study the miRNA-target interactions while few others suggested that miRNAs can anneal within the 3′UTR, 5′UTR or CDS regions of their target to regulate gene expression (Erhard et al, 2014; Yang et al, 2017; da Silveira et al, 2018). This study however brings a unique perspective through identification of conserved miRNAs targeting immune genes responding during bovine trypanosomosis by analyzing the complete sequence of the gene which include the 3′-, 5′ UTRs and the CDS.…”

“…The disease is associated with multifactorial complex phenotype, requiring an integrative biological approach to elucidate the molecular networks involved and to identify significant markers that may be useful for disease diagnosis and treatment (Noyes et al, 2011; Smetko et al, 2015; Kim et al, 2017). Recently, major research efforts are focused on utilizing microRNA (miRNA) as diagnostic biomarkers or therapeutic targets due to their involvement in various disease phenotypes and malignancies (Lawless et al, 2014; Scheel et al, 2017; Ammah et al, 2018; da Silveira et al, 2018). MicroRNAs are short, non-coding RNAs, which are capable of post-transcriptionally modifying gene expression, and form complexes with argonaute proteins that hybridize to the target mRNAs leading to the degradation of mRNA and negatively impact protein expression (Bushati and Cohen, 2007; Bartel, 2009; Meister, 2013; Huberdeau et al, 2017).…”

Frequency of MicroRNA Response Elements Identifies Pathologically Relevant Signaling Pathways in Cancers

“…MAGIA is a web-based tool for microRNA and genes integrated analysis that brings together target predictions and gene expression profiles using different functional measures for both matched and unmatched samples (Sales et al 2010). The tool miRmapper uses mRNA-microRNA predictions and a list of differentially expressed mRNAs to identify dominant microRNAs and recognizes similarities between microRNAs based on commonly regulated mRNAs (da Silveira et al 2018). HisCoM-mimi is a hierarchically structured component analysis method that models biological relationships as structured components, to efficiently yield integrated mRNA-microRNA markers (Kim et al 2018).…”

Clustering analysis of microRNA and mRNA expression data from TCGA using maximum edge-weighted matching algorithms

“…There are bioinformatics tools clustering the miRNA-mRNA interaction network [17]. Some tools are based on miRNA-mRNA expressional correlation coefficients calculated from NGS expression data [18].…”