Abstract
WaRSwap is a randomization algorithm that for the first time provides a practical network motif discovery method for large multi-layer networks, for example those that include transcription factors, microRNAs, and non-regulatory protein coding genes. The algorithm is applicable to systems with tens of thousands of genes, while accounting for critical aspects of biological networks, including self-loops, large hubs, and target rearrangements. We validate WaRSwap on a newly inferred regulatory network from Arabidopsis thaliana, and compare outcomes on published Drosophila and human networks. Specifically, sustained input switches are among the few over-represented circuits across this diverse set of eukaryotes.
Highlights
The study of system-wide genetic circuit structure is useful to discover underlying patterns that are difficult to observe by looking at a few individual interactions in isolation
The goal of network motif discovery is to identify small over-represented subgraphs within a larger graph structure. This goal is achieved by counting the number of a particular subgraph present in the biological network of interest, and comparing this number with the distribution of counts for this same subgraph over a number of randomized networks
In order to provide additional insight into how these motifs may be contributing to developmental programs, we performed an analysis using the GOStat toolset [27] to identify statistically significantly overrepresented Gene Ontology (GO) terms in the set of Arabidopsis genes that we identified as being both 1) present in at least one WaRSwap-identified motif and 2) co-expressed in the same Arabidopsis tissue as all other genes participating in the same motif
Summary
The study of system-wide genetic circuit structure is useful to discover underlying patterns that are difficult to observe by looking at a few individual interactions in isolation. Studies of transcription factor (TF) networks in yeast have shown that even in unicellular organisms, there is a strong connection between overall regulatory architecture and TF dynamics [1,2,3]. Because additional components, such as non-coding RNAs known as microRNAs (miRNAs), have been shown to play crucial roles in gene regulatory networks of nearly every eukaryote [4,5,6,7], it would be desirable to identify and analyze patterns of regulatory architecture, especially patterns that include multiple regulatory entities with a variety of biological behaviors.
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