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In introduction to HCI class.
miRTarVisPlus

Interactive visual analysis tool for microRNA-mRNA expression profile data

Sehi L'Yi, Daekyoung Jung, Minsik Oh, Bohyoung Kim, Robert J. Freishtat, Mamta Giri, Eric Hoffman, and Jinwook Seo / 2017

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- 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.


Participants

  • 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

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.

AVAILABILITY of miRTarVis+

miRTarVis+ is an enhanced version of our previous tool, miRTarVis.

Example Datasets

Tutorial

  • 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.

Known Issues & Upcomming Updates

  • 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.

CONTACT

  • 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)

AVAILABILITY of miRTarVis (old ver.)

Video

System Requirements

  • 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

Support

  • 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).

Publications