Abstract
BackgroundNetwork-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.ResultsHere, 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.ConclusionWe 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.ReviewersThis article was reviewed by Prof. Limsoon Wong, Prof. Soojin Yi, and Prof. David Kreil.Electronic supplementary materialThe online version of this article (doi:10.1186/s13062-016-0112-y) contains supplementary material, which is available to authorized users.
Highlights
Given the huge volume and complexity of omics data such as those from TCGA (The Cancer Genome Atlas) projects, it is a major challenge to gain insights into biological principles [1]
Several algorithms for network analysis were implemented and they are tightly coupled with the visualization tools in a unified platform
User case study To demonstrate the utility of MONGKIE as general network analysis and visualization software, we analyzed multi-omics data from the TCGA Glioblastoma Multiforme (GBM) consortium [7]
Summary
Given the huge volume and complexity of omics data such as those from TCGA (The Cancer Genome Atlas) projects, it is a major challenge to gain insights into biological principles [1]. All components for visualization, network analysis for defining subgroups, and functional interpretation of network modules can be threaded into a pipeline that allows user interaction at each step.
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