Literature DB >> 28600227

miRTarVis+: Web-based interactive visual analytics tool for microRNA target predictions.

Sehi L'Yi1, Daekyoung Jung1, Minsik Oh1, Bohyoung Kim2, Robert J Freishtat3, Mamta Giri4, Eric Hoffman5, Jinwook Seo6.   

Abstract

In this paper, we present miRTarVis+, a Web-based interactive visual analytics tool for miRNA target predictions and integrative analyses of multiple prediction results. Various microRNA (miRNA) target prediction algorithms have been developed to improve sequence-based miRNA target prediction by exploiting miRNA-mRNA expression profile data. There are also a few analytics tools to help researchers predict targets of miRNAs. However, there still is a need for improving the performance for miRNA prediction algorithms and more importantly for interactive visualization tools for an integrative analysis of multiple prediction results. miRTarVis+ has an intuitive interface to support the analysis pipeline of load, filter, predict, and visualize. It can predict targets of miRNA by adopting Bayesian inference and maximal information-based nonparametric exploration (MINE) analyses as well as conventional correlation and mutual information analyses. miRTarVis+ supports an integrative analysis of multiple prediction results by providing an overview of multiple prediction results and then allowing users to examine a selected miRNA-mRNA network in an interactive treemap and node-link diagram. To evaluate the effectiveness of miRTarVis+, we conducted two case studies using miRNA-mRNA expression profile data of asthma and breast cancer patients and demonstrated that miRTarVis+ helps users more comprehensively analyze targets of miRNA from miRNA-mRNA expression profile data. miRTarVis+ is available at http://hcil.snu.ac.kr/research/mirtarvisplus.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Expression profile; Integrative analysis; Target prediction; Visualization; mRNA; microRNA

Mesh:

Substances:

Year:  2017        PMID: 28600227     DOI: 10.1016/j.ymeth.2017.06.004

Source DB:  PubMed          Journal:  Methods        ISSN: 1046-2023            Impact factor:   3.608


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