@article {Desai093807, author = {Jigar Desai and Ryan C. Sartor and Lovely Mae Lawas and Krishna Jagadish S.V. and Colleen J. Doherty}, title = {Incorporating the Rate of Transcriptional Change Improves Construction of Transcription Factor Regulatory Networks}, elocation-id = {093807}, year = {2016}, doi = {10.1101/093807}, publisher = {Cold Spring Harbor Laboratory}, abstract = {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 random-forest based network construction approach 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 To understand how organisms can turn a collection of genes into a physiological response, we need to understand how certain genes are turned on and off. Transcription factors (TFs) have a huge role in gene regulation and control how genes coordinate daily activities, developmental processes, stress responses, and many other cellular processes. In model organisms, the ability to identify direct targets of TFs via ChIP-Seq in a high-throughput manner has advanced our understanding of transcriptional regulatory networks and how organisms regulate gene expression. However, for non-model organisms, the challenge of assessing TF{\textendash}target relationships remains a limiting factor to understanding regulatory control. Computational approaches to identify regulator-target relationships in silico from easily attainable transcriptional data offer a solution. We observed that transcriptional assays are often performed as a time series and that most computational approaches do not take full advantage of the information available in time series data. Therefore, we have implemented this approach, which modifies the input into network identification algorithms and improves their ability to predict regulator-target interactions. Our approach essentially weights the expression value of each time point by the slope change after that time point so that relationships between regulators and targets are emphasized at the time points when the transcript levels are changing. We hope this improvement will increase the ability to identify regulators of interest in non-model species.}, URL = {https://www.biorxiv.org/content/early/2016/12/24/093807}, eprint = {https://www.biorxiv.org/content/early/2016/12/24/093807.full.pdf}, journal = {bioRxiv} }