Incorporating the Rate of Transcriptional Change Improves Construction of Gene Regulatory Networks

Transcriptional regulatory networks (TRNs) can be developed by computational approaches that infer regulator-target gene interactions from transcriptional assays. Successful algorithms that generate predictive, accurate TRNs enable the identification of regulator-target relationships in conditions where experimentally determining regulatory interactions is a challenge. Improving the ability of TRNs to successfully predict known regulator-target relationships in model species will enhance confidence in applying these approaches to determine regulator-target interactions in non-model species where experimental validation is challenging. Many transcriptional profiling experiments are performed across multiple time points; therefore we sought to improve regulator-target predictions by adjusting how time is incorporated into the network. We created ExRANGES, which incorporates Expression in a Rate-Normalized GEne Specific manner that adjusts how expression data is provided to the network algorithm. We tested this on a two different network construction approaches and found that ExRANGES prioritizes targets differently than traditional expression and improves the ability of these networks to accurately predict known regulator targets. ExRANGES improved the ability to correctly identify targets of transcription factors in large data sets in four different model systems: mouse, human, Arabidopsis, and yeast. Finally, we examined the performance of ExRANGES on a small data set from field-grown Oryza sativa and found that it also improved the ability to identify known targets even with a limited data set. Author Summary In model organisms, the ability to identify direct targets of transcription factors (TFs) via high throughput experimental assays has advanced our understanding of transcriptional regulatory networks and how organisms regulate gene expression. However, for non-model organisms, it remains a challenge to identify TF–target relationships through experimental approaches such as ChIP-Seq, thus limiting the ability to understand regulatory control is limited. Computational approaches to identify regulator-target relationships in silico from easily attainable transcriptional data offer a solution. Most algorithms for identifying gene regulatory networks from time series data weigh the relationship between regulators and putative targets at all time points equally. However, many regulators may control a single target in response to different inputs. Our approach, ExRANGES, focuses on time points where there is a significant change in expression to identify the association between regulators and targets. ExRANGES essentially weights the expression value of each time point by the slope change after that time point, thereby emphasizing the relationship between regulators and targets at the time points when the transcript levels are changing. We show that this change to the way expression data is included into gene regulatory network algorithms improves the identification of regulator-target interactions and we hope this will improve in silico identification of regulatory relationships in many species.