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miRmapper: A Tool for Interpretation of miRNA–mRNA Interaction Networks
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 modul...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6162471/ https://www.ncbi.nlm.nih.gov/pubmed/30223528 http://dx.doi.org/10.3390/genes9090458 |
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author | da Silveira, Willian A. Renaud, Ludivine Simpson, Jonathan Glen, William B. Hazard, Edward. S. Chung, Dongjun Hardiman, Gary |
author_facet | da Silveira, Willian A. Renaud, Ludivine Simpson, Jonathan Glen, William B. Hazard, Edward. S. Chung, Dongjun Hardiman, Gary |
author_sort | da Silveira, Willian A. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6162471 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-61624712018-10-10 miRmapper: A Tool for Interpretation of miRNA–mRNA Interaction Networks da Silveira, Willian A. Renaud, Ludivine Simpson, Jonathan Glen, William B. Hazard, Edward. S. Chung, Dongjun Hardiman, Gary Genes (Basel) Article 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. MDPI 2018-09-14 /pmc/articles/PMC6162471/ /pubmed/30223528 http://dx.doi.org/10.3390/genes9090458 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article da Silveira, Willian A. Renaud, Ludivine Simpson, Jonathan Glen, William B. Hazard, Edward. S. Chung, Dongjun Hardiman, Gary miRmapper: A Tool for Interpretation of miRNA–mRNA Interaction Networks |
title | miRmapper: A Tool for Interpretation of miRNA–mRNA Interaction Networks |
title_full | miRmapper: A Tool for Interpretation of miRNA–mRNA Interaction Networks |
title_fullStr | miRmapper: A Tool for Interpretation of miRNA–mRNA Interaction Networks |
title_full_unstemmed | miRmapper: A Tool for Interpretation of miRNA–mRNA Interaction Networks |
title_short | miRmapper: A Tool for Interpretation of miRNA–mRNA Interaction Networks |
title_sort | mirmapper: a tool for interpretation of mirna–mrna interaction networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6162471/ https://www.ncbi.nlm.nih.gov/pubmed/30223528 http://dx.doi.org/10.3390/genes9090458 |
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