Abstract 3976: NetGestalt: A web application for the integration of multidimensional omics data within a network context
Authors: Zhiao Shi, Jing Wang, Bing Zhang
Journal: Cancer Research (2012)

Abstract Advancements in high-throughput omics technologies are revolutionizing the field of cancer studies. As a prime example, The Cancer Genome Atlas (TCGA) project will generate multiple types of genomic data from 500 cases of human cancer for each of the 25 selected tumor types by 2014. Until computational tools are available for biologists and clinicians to independently interpret the vast amount of interconnected data, the potential of this type of large collaborative projects will not be fully realized. To fill the gap between data generation and investigators’ ability to interpret the data, we have developed NetGestalt, a web-based application that enables the integration of multidimensional omics data within the context of a protein interaction network. Because molecular alterations at DNA, RNA, and protein levels exert their effects primarily through changing the activity of proteins and their participating networks, protein-protein interaction network has become a powerful model for the visualization and integration of different types of molecular data. However, the standard graph-based network visualization becomes inadequate as network size and data complexity increase. We address this challenge through exploiting the inherent hierarchical architecture of the protein interaction network. By using only the horizontal dimension of a webpage to layout genes according to the hierarchical network architecture, it allows users to simultaneously compare and correlate information from experimental data, network modules, and existing knowledge rendered as tracks along the vertical dimension of the webpage, similar to the widely used genome browsers. However, without constraining the system to genomic sequence-based coordinates, NetGestalt is able to reveal functional relationship between different genes as encoded in the network. We also employed efficient software architecture to enable fast track rendering process and smooth navigation between different resolution scales from individual genes to the whole network. The potential of NetGestalt was demonstrated using the recently published TCGA ovarian cancer data with multiple types of genomic measurements on around 500 tumor samples and corresponding normal controls. In summary, we have developed NetGestalt, a novel data integration framework that allows simultaneous presentation of large scale experimental and annotation data from various sources in the context of a biological network to facilitate data visualization, analysis, interpretation and hypothesis generation. A NetGestalt-based TCGA data browser can facilitate biologists and clinicians to translate the vast amount of data into novel biological discoveries and better cancer therapeutics. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3976. doi:1538-7445.AM2012-3976