MONGKIE: An integrated tool for network analysis and visualization for multi-omics data

Yeongjun Jang, Namhee Yu, Jihae Seo, Sun Kim, Sanghyuk Lee

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Background: Network-based integrative analysis is a powerful technique for extracting biological insights from multilayered omics data such as somatic mutations, copy number variations, and gene expression data. However, integrated analysis of multi-omics data is quite complicated and can hardly be done in an automated way. Thus, a powerful interactive visual mining tool supporting diverse analysis algorithms for identification of driver genes and regulatory modules is much needed. Results: Here, we present a software platform that integrates network visualization with omics data analysis tools seamlessly. The visualization unit supports various options for displaying multi-omics data as well as unique network models for describing sophisticated biological networks such as complex biomolecular reactions. In addition, we implemented diverse in-house algorithms for network analysis including network clustering and over-representation analysis. Novel functions include facile definition and optimized visualization of subgroups, comparison of a series of data sets in an identical network by data-to-visual mapping and subsequent overlaying function, and management of custom interaction networks. Utility of MONGKIE for network-based visual data mining of multi-omics data was demonstrated by analysis of the TCGA glioblastoma data. MONGKIE was developed in Java based on the NetBeans plugin architecture, thus being OS-independent with intrinsic support of module extension by third-party developers. Conclusion: We believe that MONGKIE would be a valuable addition to network analysis software by supporting many unique features and visualization options, especially for analysing multi-omics data sets in cancer and other diseases. Reviewers: This article was reviewed by Prof. Limsoon Wong, Prof. Soojin Yi, and Maciej M Kańduła (nominated by Prof. David P Kreil).

Original languageEnglish
Article number10
JournalBiology Direct
Volume11
Issue number1
DOIs
StatePublished - 18 Mar 2016

Bibliographical note

Funding Information:
We appreciate Dr. Kyoohyoung Rho and colleagues at Korean Bioinformation Center (KOBIC) for helpful discussion in developing MONGKIE. This work has been supported by the grants from the National Research Foundation of Korea (NRF-2014M3C9A3065221, NRF-2015K1A4A3047851) and the Technology Innovation Program of the Ministry of Trade, Industry and Energy, Republic of Korea (10050154).

Publisher Copyright:
© 2016 Jang et al.

Keywords

  • Graph clustering
  • Network modeling
  • Network visualization
  • Omics data analysis
  • Over-representation analysis

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