Abstract
BackgroundTime-course gene expression profiles are frequently used to provide insight into the changes in cellular state over time and to infer the molecular pathways involved. When combined with large-scale molecular interaction networks, such data can provide information about the dynamics of cellular response to stimulus. However, few tools are currently available to predict a single active gene sub-network from time-course gene expression profiles.ResultsWe introduce a tool, TimeXNet, which identifies active gene sub-networks with temporal paths using time-course gene expression profiles in the context of a weighted gene regulatory and protein-protein interaction network. TimeXNet uses a specialized form of the network flow optimization approach to identify the most probable paths connecting the genes with significant changes in expression at consecutive time intervals. TimeXNet has been extensively evaluated for its ability to predict novel regulators and their associated pathways within active gene sub-networks in the mouse innate immune response and the yeast osmotic stress response. Compared to other similar methods, TimeXNet identified up to 50% more novel regulators from independent experimental datasets. It predicted paths within a greater number of known pathways with longer overlaps (up to 7 consecutive edges) within these pathways. TimeXNet was also shown to be robust in the presence of varying amounts of noise in the molecular interaction network.ConclusionsTimeXNet is a reliable tool that can be used to study cellular response to stimuli through the identification of time-dependent active gene sub-networks in diverse biological systems. It is significantly better than other similar tools. TimeXNet is implemented in Java as a stand-alone application and supported on Linux, MS Windows and Macintosh. The output of TimeXNet can be directly viewed in Cytoscape. TimeXNet is freely available for non-commercial users.
Highlights
Time-course gene expression profiles are frequently used to provide insight into the changes in cellular state over time and to infer the molecular pathways involved
TimeXNet is implemented in Java as a stand-alone application and supported on Linux, MS Windows and Macintosh
The output of TimeXNet can be directly viewed in Cytoscape
Summary
Time-course gene expression profiles are frequently used to provide insight into the changes in cellular state over time and to infer the molecular pathways involved. Few tools are currently available to predict a single active gene sub-network from time-course gene expression profiles. Timecourse gene expression profiles are especially useful in such studies since they capture the changes occurring in the cell over time. This data is often combined with protein-protein and protein-DNA interaction networks to identify sub-networks of activated genes [1]. We show that TimeXNet is faster and more accurate at predicting active gene sub-networks than other existing tools in the study of cellular systems as diverse as the innate immune response in mouse and the yeast osmotic stress response
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