miRTarVis+ has been introduced at a tutorial of miRNAtools which is an advanced teaching utilities using miRNA web of knowledge (Jul. 30, 2017). miRNAtools are being used in advanced teaching courses for Ph.D. students and post-docs by Francisco J. Enguita ─── Ewa Ł. Stępień, Marina C. Costa, and Francisco J. Enguita. miRNAtools: Advanced Training Using the miRNA Web of Knowledge. Non-Coding RNA 4.1 (2018): 5, http://www.mdpi.com/2311-553X/4/1/5
- Sehi L’Yi, Seoul National University, Seoul, Republic of Korea
- Daekyoung Jung, Seoul National University, Seoul, Republic of Korea
- Minsik Oh, Seoul National University, Seoul, Republic of Korea
- Bohyoung Kim, Hankuk University of Foreign Studies, Yongin-si, Republic of Korea
- Robert J. Freishtat, Children’s National Medical Center, Washington, D. C., USA
- Mamta Giri, Children’s National Medical Center, Washington, D. C., USA
- Eric Hoffman, Children’s National Medical Center, Washington, D. C., USA
- Jinwook Seo, Seoul National University, Seoul, Republic of Korea
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.
miRTarVis+ is an enhanced version of our previous tool, miRTarVis.
- Link to miRTarVis+
- Recommend using the latest version of Chrome or Safari (or possibly, Firefox) to use miRTarVis+.
- Currently, it is NOT TESTED for Internet Explorer (IE).
- Paired two-sample
- Unpaired two-sample
- P-value and Fold Change
- Other examples will be also uploaded here.
- Link to tutorial
- Some main features such as the Prediction Overview are not yet covered in the tutorial. We will update the tutorial as soon as possible.
- We found that when there are many columns in paired two-sample data, miRTarVis+ is sometimes generating wrong p-value for paired t-test.
- We will improve performance for GenMiR++
- Optimal Label Placement has performance issues in Internet Explorer.
- If you find any bugs or problems when using miRTarVis+, please let us know so that we can fix the issues.
- Email: shlyi@hcil.snu.ac.kr (Sehi L'Yi)
- Excutable files, sample datasets, and tutorials are available at hcil.snu.ac.kr/~rati/miRTarVis/index.html.
- Java Runtime Environment (JRE) version 7+ (64-bit version is recommended, because 32-bit version JRE has memory limits)
- Any OS that supports JRE 7+
- Minimum screen resolution: 1280 x 720
- This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIP) (No. NRF-2014R1A2A2A03006998 and NRF-2016R1A2B2007153) and by the Hankuk University of Foreign Studies Research Fund. The ICT at Seoul National University provided research facilities for this study.
- This work was partly supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government of MSIP (No. NRF-2014R1A2A2A03006998) and by the Korea government of MEST (No. NRF-2011-0030813). This work was also supported by the Clark Family Foundation (USA) and the National Institutes of Health (3R01 NS29525).
- This work was supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government(MEST) (No. NRF-2011-0030813).