Improving Gene Regulatory Network Inference by Incorporating Rates of Transcriptional Changes

• Published in: Scientific reports Vol. 7; no. 1; pp. 17244 - 12
• Main Authors: Desai, Jigar S., Sartor, Ryan C., Lawas, Lovely Mae, Jagadish, S. V. Krishna, Doherty, Colleen J.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 08-12-2017; Nature Publishing Group
• Subjects: 38/39; 631/337/2019; 631/553/2711; Algorithms; Animals; Circadian Clocks - genetics; Circadian rhythm; Computational Biology - methods; Computer applications; Datasets; Gene expression; Gene Regulatory Networks; Humanities and Social Sciences; Humans; Identification; Mice; multidisciplinary; Ribonucleic acid; RNA; Science; Science (multidisciplinary); Species; Time series; Transcription factors; Transcription Factors - metabolism; Transcription, Genetic
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-017-17143-1

Organisms respond to changes in their environment through transcriptional regulatory networks (TRNs). The regulatory hierarchy of these networks can be inferred from expression data. Computational approaches to identify TRNs can be applied in any species where quality RNA can be acquired, However, ChIP-Seq and similar validation methods are challenging to employ in non-model species. Improving the accuracy of computational inference methods can significantly reduce the cost and time of subsequent validation experiments. We have developed ExRANGES, an approach that improves the ability to computationally infer TRN from time series expression data. ExRANGES utilizes both the rate of change in expression and the absolute expression level to identify TRN connections. We evaluated ExRANGES in five data sets from different model systems. ExRANGES improved the identification of experimentally validated transcription factor targets for all species tested, even in unevenly spaced and sparse data sets. This improved ability to predict known regulator-target relationships enhances the utility of network inference approaches in non-model species where experimental validation is challenging. We integrated ExRANGES with two different network construction approaches and it has been implemented as an R package available here:
http://github.com/DohertyLab/ExRANGES
. To install the package type: devtools::install_github(“DohertyLab/ExRANGES”).