title: MONGKIE: an integrated tool for network analysis and visualization for multi-omics data.
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.
  
  
  CONCLUSIONS
  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. .
authors.affiliation: Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul, Republic of Korea.|Interdisciplinary Program in Bioinformatics, College of Natural Science, Seoul National University, Seoul, Republic of Korea.
authors.forename: Yeongjun
authors.initials: Y
authors.lastname: Jang
authors.affiliation: Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul, Republic of Korea.
authors.forename: Namhee
authors.initials: N
authors.lastname: Yu
authors.affiliation: Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul, Republic of Korea.
authors.forename: Jihae
authors.initials: J
authors.lastname: Seo
authors.affiliation: Interdisciplinary Program in Bioinformatics, College of Natural Science, Seoul National University, Seoul, Republic of Korea. sunkim.bioinfo@snu.ac.kr.
authors.forename: Sun
authors.initials: S
authors.lastname: Kim
authors.affiliation: Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul, Republic of Korea. sanghyuk@ewha.ac.kr.
authors.forename: Sanghyuk
authors.initials: S
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D056915:DNA Copy Number Variations / Q000235:genetics; D012984:Software
keywords: Graph clustering; Network modeling; Network visualization; Omics data analysis; Over-representation analysis