RT Journal Article
SR Electronic
T1 Improving Gene Regulatory Network Inference by Incorporating Rates of Transcriptional Changes
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 093807
DO 10.1101/093807
A1 Jigar S. Desai
A1 Ryan C. Sartor
A1 Lovely Mae Lawas
A1 SV Krishna Jagadish
A1 Colleen J. Doherty
YR 2017
UL http://biorxiv.org/content/early/2017/07/13/093807.abstract
AB Motivation Transcriptional regulatory networks (TRNs) are hierarchies of regulatory factors that control the expression levels of target genes. The signal transduction pathways of these networks are often determined by experimental analysis. Computational inference of the connections between regulators and target genes using transcriptional assays can identify high confidence candidate regulator-target relationships. Often, expression experiments designed for this purpose are performed in a time series. Most TRN identifying algorithms, however, do not take full advantage of that temporal data. We developed a new approach, ExRANGES, which utilizes both the rate of change in expression and the absolute expression level to identify TRN connections.Results Our novel strategy, ExRANGES improves the ability to computationally infer TRN from time series expression data by emphasizing the comparison between regulator and target at time points where there is a significant change in expression. ExRANGES combines the rate of change in expression with the absolute expression level and improves the ability to accurately identify known targets of transcriptional regulators. We evaluated ExRANGES in four large data sets from different model systems and in one sparse data set using two different network construction approaches. ExRANGES improved the identification of experimentally validated transcription factor targets for all species even in unevenly spaced and sparse data sets. This improved ability to predict known regulator-target relationships in model species enhances the utility of network inference approaches in non-model species where experimental validation is challenging.Availability ExRANGES has been implemented as an R package and is available http://github.com/DohertyLab/ExRANGESTo install the package type devtools::install_github("DohertyLab/ExRANGES")Contact colleen_doherty{at}ncsu.eduSupplementary information is available on-line.