miRNet - dissecting miRNA-target interactions and functional associations through network-based visual analysis

By Yannan Fan, Keith Siklenka, Simran K. Arora, P. Ribeiro, S. Kimmins, Jianguo Xia. Nucleic Acids Res., 2016

397 citations recorded by the source corpus.

MicroRNAs (miRNAs) can regulate nearly all biological processes and their dysregulation is implicated in various complex diseases and pathological conditions. Recent years have seen a growing number of functional studies of miRNAs using high-throughput experimental technologies, which have produced a large amount of high-quality data regarding miRNA target genes and their interactions with small molecules, long non-coding RNAs, epigenetic modifiers, disease associations, etc. These rich sets of information have enabled the creation of comprehensive networks linking miRNAs with various biologically important entities to shed light on their collective functions and regulatory mechanisms. Here, we introduce miRNet, an easy-to-use web-based tool that offers statistical, visual and network-based approaches to help researchers understand miRNAs functions and regulatory mechanisms. The key features of miRNet include: (i) a comprehensive knowledge base integrating high-quality miRNA-target interaction data from 11 databases; (ii) support for differential expression analysis of data from microarray, RNA-seq and quantitative PCR; (iii) implementation of a flexible interface for data filtering, refinement and customization during network creation; (iv) a powerful fully featured network visualization system coupled with enrichment analysis. miRNet offers a comprehensive tool suite to enable statistical analysis and functional interpretation of various data generated from current miRNA studies. miRNet is freely available at http://www.mirnet.ca.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1fc2570a94dc7985c2fb6203dae7125e20fd194f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Jianguo Xia
2. Le Chang
3. Guangyan Zhou
4. Othman Soufan
5. Giulia Fiscon
6. Paola Paci
7. V. Licursi
8. Federica Conte

In Molecular Biology

1. Guangyan Zhou
2. Othman Soufan
3. Le Chang
4. Jianguo Xia
5. Giulia Fiscon
6. Paola Paci
7. V. Licursi
8. Federica Conte

Citations in the corpus

• MIENTURNET: an interactive web tool for microRNA-target enrichment and network-based analysis (2019, methodology): “Among them, the most used are miTEA [12], GSEA [13], miEAA [14], miRNet [15], and miRTargetLink [16].”
• miRNet 2.0: network-based visual analytics for miRNA functional analysis and systems biology (2020, background): “Other features can be found in our prior publications (9,10,21).”