Abstract
BackgroundThe misregulation of microRNA (miRNA) has been shown to cause diseases. Recently, we have proposed a computational method based on a random walk framework on a miRNA-target gene network to predict disease-associated miRNAs. The prediction performance of our method is better than that of some existing state-of-the-art network- and machine learning-based methods since it exploits the mutual regulation between miRNAs and their target genes in the miRNA-target gene interaction networks.ResultsTo facilitate the use of this method, we have developed a Cytoscape app, named RWRMTN, to predict disease-associated miRNAs. RWRMTN can work on any miRNA-target gene network. Highly ranked miRNAs are supported with evidence from the literature. They then can also be visualized based on the rankings and in relationships with the query disease and their target genes. In addition, automation functions are also integrated, which allow RWRMTN to be used in workflows from external environments. We demonstrate the ability of RWRMTN in predicting breast and lung cancer-associated miRNAs via workflows in Cytoscape and other environments.ConclusionsConsidering a few computational methods have been developed as software tools for convenient uses, RWRMTN is among the first GUI-based tools for the prediction of disease-associated miRNAs which can be used in workflows in different environments.
Highlights
ResultsTo facilitate the use of this method, we have developed a Cytoscape app, named RWRMTN, to predict disease-associated miRNAs. RWRMTN can work on any miRNA-target gene network
The misregulation of microRNA has been shown to cause diseases
The overall prediction performance of RWRMTN on a set of diseases was reported in our previous study [19]
Summary
The overall prediction performance of RWRMTN on a set of diseases was reported in our previous study [19]. We performed this task with candidate miRNAs from the miRNA-target gene network constructed from TargetScan [36] based on 31 known breast cancer-associated miRNAs reported in miR2Disease [37] using Cytoscape menu and Command APIs via the four-step workflow (Fig. 3). We ranked 799 miRNAs that were differentially expressed between the 64 wild-type samples and 36 TP53 mutant breast cancer samples collected from [39] via CyREST API called in R environment using the miRNA-target gene network constructed from miRWalk [35] and the known disease-miRNA association dataset HMDD [38]. Prediction of breast cancer-associated miRNAs by calling CyREST API we first introduce some developed CyREST APIs, which provides some helpful functions We demonstrate their use in a workflow in R environment. We ranked the candidate miRNAs by RWRMTN via a CyREST API using a miRNAtarget interaction dataset miRWalk [35] and a known disease-miRNA association dataset HMDD [38] via workflow in R environment using CyREST API POST /RWRMTN/ v1/rank (See more detail in Additional file 1)
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