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. 2012 Jan;40(Database issue):D1067-76.
doi: 10.1093/nar/gkr968. Epub 2011 Nov 12.

GeneWeaver: a web-based system for integrative functional genomics

Erich J Baker  1 , Jeremy J Jay, Jason A Bubier, Michael A Langston, Elissa J Chesler
Affiliations

GeneWeaver: a web-based system for integrative functional genomics

Erich J Baker et al. Nucleic Acids Res. 2012 Jan.

Abstract

High-throughput genome technologies have produced a wealth of data on the association of genes and gene products to biological functions. Investigators have discovered value in combining their experimental results with published genome-wide association studies, quantitative trait locus, microarray, RNA-sequencing and mutant phenotyping studies to identify gene-function associations across diverse experiments, species, conditions, behaviors or biological processes. These experimental results are typically derived from disparate data repositories, publication supplements or reconstructions from primary data stores. This leaves bench biologists with the complex and unscalable task of integrating data by identifying and gathering relevant studies, reanalyzing primary data, unifying gene identifiers and applying ad hoc computational analysis to the integrated set. The freely available GeneWeaver (http://www.GeneWeaver.org) powered by the Ontological Discovery Environment is a curated repository of genomic experimental results with an accompanying tool set for dynamic integration of these data sets, enabling users to interactively address questions about sets of biological functions and their relations to sets of genes. Thus, large numbers of independently published genomic results can be organized into new conceptual frameworks driven by the underlying, inferred biological relationships rather than a pre-existing semantic framework. An empirical 'ontology' is discovered from the aggregate of experimental knowledge around user-defined areas of biological inquiry.

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Figures

Figure 1.
Figure 1.
Curation and integrative analysis of secondary data in the Ontological Discovery Environment. The overall system architecture consists of a centralized database that collects a variety of curated data and metadata and serves a suite of analysis tools. It uses a data from community resources to create clusters of gene homology across supported species, enabling ODE to rapidly translate gene sets.
Figure 2.
Figure 2.
The analyze gene sets page. GeneWeaver's analysis functions are accessed from this page. Gene sets must first be collected and stored into one or more projects by the user. In this case, a project called ‘Alcohol' contains 121 gene sets, nine of which are selected for analysis using the tools on the right. Options can be selected from this tool bar prior to executing the tool.
Figure 3.
Figure 3.
The gene set graph. The gene set graph reveals the highly connected genes among the nine gene sets selected in Figure 2. This analysis reveals DDX5 as the most highly connected gene, connected to both human and mouse alcohol-related measures. Inset: clicking on a gene node executes a search for gene sets containing the featured gene or its homologs. Clicking on a gene set node reveals the contents and metadata for that gene set.
Figure 4.
Figure 4.
The phenome graph. The phenome graph drawn from nine inputs selected in Figure 2. The phenome graph is a directed acyclic graph of the intersections of gene sets. Each node represents gene sets and the genes they share. Higher order intersections are represented in the root nodes at the top, and individual gene sets in the leaves at the bottom. Inset: clicking a node opens a page showing the intersections among gene sets in list form. Results from this page can be sent to other tools for annotation, including GAGGLE.
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