Improving Gene Regulatory Network Inference by Incorporating Rates of Transcriptional Changes.

ABSTRACT: 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 f

SUBMITTER: Desai JS 

PROVIDER: S-EPMC5722905 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature