News & Updates
  • Updated R from 3.6.2 to 4.0.2 and code refactoring (09/15/2020);
  • Added support for gradient background (09/14/2020);
  • Added support to show nodes with multiple annotations (i.e. lncRNA and Gene, TF and Gene) as piecharts (07/30/2020);
  • Users can now select the original database for miRNA gene target analysis (07/06/2020);
  • Our miRNet 2.0 paper is now accepted by NAR 2020 web server issue (05/21/2020);
  • Added support for the Human Reference Interactome (HuRI) map (05/14/2020);
  • Added detailed online tutorials (04/24/2020);
  • Updated Java (from Java8 to Java11) and Primefaces (from PF7 to PF8) (04/08/2020);
  • Added support for edge bundling to reduce edge crossing in large networks (03/28/2020);
  • Added support for p-value adjustment in Function Explorer (03/28/2020);
  • Users can easily update colors and sizes for different node types using Global Node Styles pane (03/19/2020);
  • Updated FAQs and the miRNet Overview tutorial (03/13/2020);
  • Added the "Module Explorer" panel for better network visual analytics (03/08/2020);
  • Fixed performance issue of several algorithms for large network layout (03/02/2020);
  • Added two layout algorithms (Concentric and Backbone) for multipartite networks (02/24/2020);
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Key Features
  • Support for various inputs & statistics: miRNet accepts a list of miRNAs, miR-SNPs, genes, transcription factors, small molecules, ncRNAs, diseases, epigenetic modifers, any of their combinations or a data table from microarray, RNAseq or RT-qPCR experiments. miRNet supports differential analysis using limma, edgeR and HTqPCR methods; enrichment analysis using standard hypergeometric tests and unbiased random sampling.
  • Comprehensive functional annotation: miRNet integrates data from 14 different miRNA databases - TarBase, miRTarBase, miRecords, miRanda (S mansoni only), miR2Disease, HMDD, PhenomiR, SM2miR, PharmacomiR, EpimiR, starBase, TransmiR, ADmiRE, and TAM 2.0. It currently supports Human, Mouse, Rat, Cattle, Pig, Chicken, Zebra fish, Fruit fly, C. elegans, and S. mansoni.
  • Exploring xeno-miRNAs and their potential targets: miRNet currently supports six hosts (Human, Mouse, Chicken, Fruit fly, and C. elegans) with xeno-miRNAs reported from over 50 species. It contains over 400 experimentally detected xeno-miRNAs supplemented with 1000 computational predicted transportable miRNAs. Their potential gene targets are predicted using two algorithms - miRanda and TarPmiR.
  • Creation of miRNA-target interaction networks: miRNet provides a wide array of options to allow researchers to build miRNA-target interaction networks at different confidence levels. The resulting network can be further optimized using different algorithms to improve visualization and understanding. The network algorithm includes Force Atlas, Fruchterman-Reingold, Graphopt, Large Graph, Random, Reduce Overlap, Bipartite/Tripartite, Concentric Circle, and Backbone algorithm.
  • High-performance network visual analytics: miRNet allows users to easily create miRNA-centric networks consisting of different molecules or phenotypes of interest: genes, diseases, small molecules, SNPs (affecting miRNAs or their binding sites), ncRNAs (lncRNA, sncRNA, circRNA or peudogene), epigenetic modifiers, and transcription factors. The system supports zooming, batch highlighting, point-and-click, drag-and-drop, enrichment analysis, etc. to enable users to intuitively explore miRNAs, targets and functions.
Publications
Acknowledgements
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