miRsig: a consensus-based network inference methodology to identify pan-cancer miRNA-miRNA interaction signatures

Abstract

Decoding the patterns of miRNA regulation in diseases are important to properly realize its potential in diagnostic, prog- nostic, and therapeutic applications. Only a handful of studies computationally predict possible miRNA-miRNA interactions; hence, such interactions require a thorough investigation to understand their role in disease progression. In this paper, we design a novel computational pipeline to predict the common signature/core sets of miRNA-miRNA interactions for different diseases using network inference algorithms on the miRNA-disease expression profiles; the individual predictions of these algorithms were then merged using a consensus-based approach to predict miRNA-miRNA associations. We next selected the miRNA-miRNA associations across particular diseases to generate the corresponding disease-specific miRNA-interaction networks. Next, graph intersection analysis was performed on these networks for multiple diseases to identify the common signature/core sets of miRNA interactions. We applied this pipeline to identify the common signature of miRNA-miRNA inter- actions for cancers. The identified signatures when validated using a manual literature search from PubMed Central and the PhenomiR database, show strong relevance with the respective cancers, providing an indirect proof of the high accuracy of our methodology. We developed miRsig, an online tool for analysis and visualization of the disease-specific signature/core miRNA-miRNA interactions, available at: http://bnet.egr.vcu.edu/miRsig.

Clustered miRNAs and their role in biological functions and diseases

“…Computational predictions and some experimental results have shown that miRNA cluster interactions do take place (Song et al, ). miRNA–miRNA interaction plays an important role in regulating various biological functions, and alterations are reported in many pathophysiological conditions (Cloonan, ; Nalluri et al, ). Deregulation of miRNA clusters can alter the cross talk between miRNA clusters (inter‐ and intra‐cluster interactions) as well as affecting target genes and associated signalling pathways, and can thereby greatly influence the pathophysiology of disease.…”

Co-Expression Networks for Causal Gene Identification Based on RNA-Seq Data of Corynebacterium pseudotuberculosis

“…The predicted gene interaction networks (or equivalently termed as gene coexpression networks in some cases) were built with miRsig [17]. This tool applies seven different network inference algorithms on the gene expression data that individually reverse-engineer the interaction scores between the genes.…”

“…In this study, we identified the influential or causal genes using four RNA-Seq datasets from C.pseudotuberculosis, first by using the miRsigpipeline to obtain the predicted gene coexpression network [17] and next applying the miRinfluence tool to identify the influential and causal genes inside the network [15]. We adapted the methodology in these tools to determine the critical genes that play a causal role in the signaling cascade through influence diffusion and hence may regulate the overall gene expression of the entire network.…”

Towards a Comprehensive Understanding of miRNA Regulome and miRNA Interaction Networks

“…• EpimiR [36]: This data source contains the interactions and information between the epigenetic modification and miRNAs in the context of several diseases. It also provides information about the predicted transcription start cites which will help in providing more details in miRNA guided post-transcriptional gene regulation.…”

“…The usage of network inference algorithms in the DREAM challenge, to reconstruct the gene-TF regulatory network [37] is a prime example. The prediction of disease specific miRNA-miRNA interaction network via a consensus-based network inference approach [36] is a recent example. Traditionally, these network inference algorithms have used experimentally available expression datasets as input to predict new associations based on the patterns of co-expression observed in the experiments.…”