MONGKIE

MONGKIE facilitates network-based integrative analysis and visualization of multilayered omics data, including somatic mutations, copy number variations, and gene expression, to identify driver genes and regulatory modules.

Key Features:

• Network-visualization integration: Integrates network visualization with omics data analysis to visualize and analyze complex biomolecular networks.
• Multi-omics visualization: Displays somatic mutations, copy number variations, and gene expression across networks and supports visualization and optimization of defined subgroups.
• Network analysis algorithms: Incorporates in-house algorithms including network clustering and over-representation analysis for extracting modules and candidate driver genes.
• Data comparison and overlaying: Supports comparison of multiple datasets within the same network via data-to-visual mapping and overlay functions.
• Custom interaction networks management: Allows management and use of custom interaction networks for tailored analyses.

Scientific Applications:

• Integrative multi-omics analysis: Enables joint analysis of somatic mutations, copy number variations, and gene expression to detect candidate drivers and regulatory modules.
• Cancer network analysis: Facilitates network-based visual data mining to uncover regulatory modules and driver genes in cancer and other complex diseases.
• TCGA glioblastoma case study: Demonstrated utility through application to TCGA glioblastoma data.

Methodology:

Implements in-house network analysis algorithms such as network clustering and over-representation analysis, supports data-to-visual mapping and overlay of multiple datasets, manages custom interaction networks, and is implemented in Java using the NetBeans plugin architecture.