News & Updates
  • Code cleaning and refactoring (01/24/2020);
  • Fixed issue with data filtering in the Network Tools (01/23/2020);
  • Updated tutorials and FAQs (01/02/2020);
  • Enhanced network visualization & customization. Users can now customize background color, node shape & label, edge color & opacity (01/02/2020);
  • Users can now download all the results from the Download page (12/20/2019);
  • Added support for bipartite and tripartite network layout building and visualization (12/19/2019);
  • Added a new module to support integrating multiple types of molecules (miRNAs, TFs, genes, lncRNAs) for system-level visual analytics (12/18/2019);
  • Added a new module to support miRNA SNP annotation and targets interactions (12/13/2019);
  • Added a new module to support miRNA and transcription factor interactions (12/03/2019);
  • Updated miRNA-disease interaction data based on new release of HMDD v3.2 (11/08/2019);
  • Updated R from 3.5.1 to the latest version (3.6.1) (10/16/2019);
  • Added support for pig (429 miRNAs and 8353 target genes) (10/09/2019);
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Key Features
  • Support for various inputs & statistics: miRNet accepts a list of miRNAs, miR-SNPs or targets, 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 thirteen different miRNA databases - TarBase, miRTarBase, miRecords, miRanda (S mansoni only), miR2Disease, HMDD, PhenomiR, SM2miR, PharmacomiR, EpimiR, starBase, TransmiR, and ADmiRE. 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, and Bipartite/Tripartite algorithm.
  • High-performance network visual analytics: miRNet offers seven types of networks on miRNA-gene, miRNA-disease, miRNA-small molecule, miRNA-lncRNA, miRNA-epigenetic modifier, and miRNA-transcription factor. The system supports zooming, 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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