FORG3D: Force-directed 3D graph editor for visualization of integrated genome scale data

Jussi Paananen,Garry Wong

doi:10.1186/1752-0509-3-26

Abstract

BackgroundGenomics research produces vast amounts of experimental data that needs to be integrated in order to understand, model, and interpret the underlying biological phenomena. Interpreting these large and complex data sets is challenging and different visualization methods are needed to help produce knowledge from the data.ResultsTo help researchers to visualize and interpret integrated genomics data, we present a novel visualization method and bioinformatics software tool called FORG3D that is based on real-time three-dimensional force-directed graphs. FORG3D can be used to visualize integrated networks of genome scale data such as interactions between genes or gene products, signaling transduction, metabolic pathways, functional interactions and evolutionary relationships. Furthermore, we demonstrate its utility by exploring gene network relationships using integrated data sets from a Caenorhabditis elegans Parkinson's disease model.ConclusionWe have created an open source software tool called FORG3D that can be used for visualizing and exploring integrated genome scale data.

Highlights

Genomics research produces vast amounts of experimental data that needs to be integrated in order to understand, model, and interpret the underlying biological phenomena
Interaction data was obtained from a study describing genetic interactions in Caenorhabditis elegans [9] and combined with a whole genome C. elegans gene expression microarray data set obtained from a transgenic Parkinson's Disease model compared to wild type worms [18,19], which was combined with functional gene annotation information from Wormbase [20]
Query null mutant strains were subjected to RNA interference (RNAi) screens and animals that displayed synthetic phenotypes from RNAi were scored as having interaction

Summary

Introduction

Genomics research produces vast amounts of experimental data that needs to be integrated in order to understand, model, and interpret the underlying biological phenomena. Interpreting these large and complex data sets is challenging and different visualization methods are needed to help produce knowledge from the data. To understand the biological phenomena behind systems biology data, researchers often need to combine different kinds of experimental results, creating complex data sets of integrated information. Protein-protein interaction maps indicate a high level of molecular connectivity between different biological pathways highlighting the inter-related functions of many biological processes [8]. The ability to construct, analyze, and interpret integrated-omics data is fundamental to understanding gene function in systems biology. To help researchers to visualize and (page number not for citation purposes)

Results

Discussion

Conclusion