Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks

Abstract

Genomics provided us with an unprecedented quantity of data on the genes that are activated or repressed in a wide range of phenotypes. We have increasingly come to recognize that defining the networks and pathways underlying these phenotypes requires both the integration of multiple data types and the development of advanced computational methods to infer relationships between the genes and to estimate the predictive power of the networks through which they interact. To address these issues we have developed Predictive Networks (PN), a flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these 'known' interactions together with gene expression data to infer robust gene networks. The PN web application is accessible from http://predictivenetworks.org. The PN code base is freely available at https://sourceforge.net/projects/predictivenets/.

Figure 1.

Overview of the core PN concepts ultimately representing a gene interaction network. A gene interaction network (A) is a collection of triples (B) where each triple involves two genes (for example PGC, SIRT1) and a predicate (for example ‘is inhibited by’); each gene is described by a number of meta-data, incuding annotations; each gene can be part of a users’ gene list.

Figure 2.

Text mining pipeline. PubMed abstracts and full-text articles from PubMed Central are extracted and formatted so they can be analyzed using the common text interface. The processed text is then mined using a combination of custom parsing scripts and the LingPipe text processing library to identify gene interaction triples and their contexts. These triples are then used to infer gene interactions networks used throughout PN.

Figure 3.

Front page of the PN web application displaying the four entry points: the single gene, single gene–gene interaction and gene list searches, and the network inference analysis panel. The top left panel provides a series of quick links to ensure easy navigation between the different web pages which compose the PN web application.

Figure 4.

Gene interaction network inferred from breast cancer gene expression data and the PIK3CA signature. The network graph in the upper panel allows users to view the topology of the inferred gene interaction network. Each gene–gene interaction is color-coded to represent the evidence supporting it: literature-inferred interactions that are not supported by reported PN triples are colored in red, those inferred from the data only are green, and those supported by both are yellow. In the lower panel a color-coded heatmap allows users to quickly identify clusters of interactions and click on an interaction of interest to highlight it on the network graph.

Figure 5.

Interaction- and gene-specific statistics for the PIK3CA gene interaction network. The interaction-specific stability scores are represented in (A) and can be displayed in the PN application by mousing over the heatmap. One can see that many interactions are unstable (stability < 0.5) meaning that they cannot be confidently inferred from the data. However, some such as SCGB2A2SCGB1D2 are highly stable given the data. The gene-specific R² prediction scores are displayed in (B) where one can see that some genes can be accurately predicted given their parents, see SCGB2A2, SCGB1D2, RPL14, PITX1, IRS2, MYC and NOTCH2 for instance.