L'Yi Sehi, Jung Daekyoung, Oh Minsik, Kim Bohyoung, Freishtat Robert J, Giri Mamta, Hoffman Eric, Seo Jinwook
Department of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea.
Division of Biomedical Engineering, Hankuk University of Foreign Studies, 81 Oedae-ro, Mohyeon-myeon, Cheoin-gu, Yongin-si, Gyeonggi-do 449-791, Republic of Korea.
Methods. 2017 Jul 15;124:78-88. doi: 10.1016/j.ymeth.2017.06.004. Epub 2017 Jun 7.
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.
在本文中，我们展示了miRTarVis+，这是一款基于网络的交互式可视化分析工具，用于miRNA靶标预测以及对多个预测结果的综合分析。已经开发了各种微小RNA（miRNA）靶标预测算法，通过利用miRNA-信使核糖核酸（mRNA）表达谱数据来改进基于序列的miRNA靶标预测。也有一些分析工具可帮助研究人员预测miRNA的靶标。然而，仍然需要提高miRNA预测算法的性能，更重要的是需要用于对多个预测结果进行综合分析的交互式可视化工具。miRTarVis+具有直观的界面，以支持加载、过滤、预测和可视化的分析流程。它可以通过采用贝叶斯推理和基于最大信息的非参数探索（MINE）分析以及传统的相关性和互信息分析来预测miRNA的靶标。miRTarVis+通过提供多个预测结果的概述，然后允许用户在交互式树形图和节点链接图中检查选定的miRNA-mRNA网络，来支持对多个预测结果的综合分析。为了评估miRTarVis+的有效性，我们使用哮喘和乳腺癌患者的miRNA-mRNA表达谱数据进行了两个案例研究，并证明miRTarVis+有助于用户从miRNA-mRNA表达谱数据中更全面地分析miRNA的靶标。可通过http://hcil.snu.ac.kr/research/mirtarvisplus获取miRTarVis+。
