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Genes 2018, 9(9), 458; https://doi.org/10.3390/genes9090458

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

1
Center for Genomic Medicine, Bioinformatics, Medical University of South Carolina (MUSC), Charleston, SC 29425, USA
2
Division of Nephrology, Department of Medicine, Medical University of South Carolina (MUSC), Charleston, SC 29425, USA
3
Laboratory for Marine Systems Biology, Hollings Marine Laboratory, Charleston, SC 29412, USA
4
Academic Affairs Faculty, Medical University of South Carolina (MUSC), Charleston, SC 29425, USA
5
Department of Public Health Sciences, Medical University of South Carolina (MUSC), Charleston, SC 29425, USA
6
Institute for Global Food Security, Queens University Belfast, Stranmillis Road, Belfast BT9 5AG, UK
*
Author to whom correspondence should be addressed.
Received: 28 August 2018 / Revised: 7 September 2018 / Accepted: 7 September 2018 / Published: 14 September 2018
(This article belongs to the Special Issue Systems Analytics and Integration of Big Omics Data)
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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. View Full-Text
Keywords: bioinformatics pipelines; algorithm development for network integration; miRNA–gene expression networks; multiomics integration; network topology analysis bioinformatics pipelines; algorithm development for network integration; miRNA–gene expression networks; multiomics integration; network topology analysis
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da Silveira, W.A.; Renaud, L.; Simpson, J.; Glen, W.B., Jr.; Hazard, E.S.; Chung, D.; Hardiman, G. miRmapper: A Tool for Interpretation of miRNA–mRNA Interaction Networks. Genes 2018, 9, 458.

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