Time-series expression data from genes or proteins always provides us with an indispensable understanding of the temporal dynamics of biological systems. Integrating multi-omics data with biological networks like gene-gene association, protein-protein interaction drug-disease interaction, etc., may be a crucial and effective approach for the interpretation of dynamic expression changes of genes in evolving conditions. The network-based temporal analysis briefs about the temporal changes in molecular pathways, help to characterize molecular activity in circadian rhythm or cell cycle and plays a key role in the cellular stimulus.
Assimilating temporal expression level information with a biological network is more challenging. In order to gain a broader understanding, interactomes need to be integrated with temporal -omics data. Popular network analysis approaches combine time-varying expression data in network motif finding, active subnetworks identification, community detection, identification of active subnetworks, and identify changes in network features in terms of several centrality measures.
Most of the multi-omics data integration tools focused mainly on the static nature of the networks and thus are not applicable in temporal network data. Temporal network analysis tries to determine the regulating factors by exploring the network with varying time duration structure considering the time as a key component of the structure. Recently, the developed tool, TimeNexus, is able to estimate time-series data on the sequential layers of any multilayer network. TimeNexus allows users to efficiently create, visualize and manage time-varying multilayer networks by employing different combinations of edge and node tables.
The basic idea of TimeNexus is to present a flexible and easy-to-use app that can utilize temporal data in network analysis. TimeNexusis a model that represents a temporal network from a discrete-time longitudinal network in which the time-varying expression changes are projected on different layers of a multilayer network. Expression changes of any point of time are projected on any layer in the state of node weights where the layers maintain the temporal ordering of data. However, in TimeNexus, inter-layer edges (the connection between time-varying networks) are employed as transition states between nodes from one layer to its next layer to get the full benefit of the time-series data.
TimeNexus is linked with Cytoscape apps PathLinker and AnatApp/the ANAT server to extract influential subnetwork from time-series data. PathLinker finds k (based on user’s choice) number of shortest paths from any source to the destination node using a modified version of Yen’s algorithm for the shortest path and finally identifies active subnetworks/motifs by associating these shortest paths. ANAT extracts functional subnetworks from large-scale interactome data by combining a set of target nodes (gene/protein) with anchor nodes (basically the hub proteins).
The most interesting thing about TimeNexus is that it can be extended with any other network analysis tool of Cytoscape, through its programmatic interface. More especially whenever the temporal analysis is a more crucial need for any complex network-based bioinformatics problem or any other domain.