Network‐based approaches for understanding gene regulation and function in plants

Abstract

Expression reprogramming directed by transcription factors is a primary gene regulation underlying most aspects of the biology of any organism. Our views of how gene regulation is coordinated are dramatically changing thanks to the advent and constant improvement of high-throughput profiling and transcriptional network inference methods: from activities of individual genes to functional interactions across genes. These technical and analytical advances can reveal the topology of transcriptional networks in which hundreds of genes are hierarchically regulated by multiple transcription factors at systems level. Here we review the state of the art of experimental and computational methods used in plant biology research to obtain large-scale datasets and model transcriptional networks. Examples of direct use of these network models and perspectives on their limitations and future directions are also discussed.

WGCNA Analysis Identifies the Hub Genes Related to Heat Stress in Seedling of Rice (Oryza sativa L.)

“…However, due to the complexity of phenotypes under HS, relying on traditional map–based cloning to analyze the molecular mechanism of HS requires a significant amount of labor power and material resources. In recent years, the combination analysis of RNA–seq and WGCNA has already become a critical, cost effective method to discover the key genes and interactions that might be functionally related to stress [ 26 ]. The main purpose of this study was to reveal the different molecular mechanisms of the two genotypes, and to identify the hub genes in response to HS.…”

Transcriptional regulation of plant innate immunity

“…For this, (i) different immune-stimulatory treatments should be compared and (ii) different whole-genome, multi-omics data sets should be combined, (iii) followed by advanced integrated data analysis including the use of mathematical modeling tools (Figure 2). Examples of these omics assays are profiling of chromatin accessibility, RNA variants (mRNA, miRNA, lncRNA, etc), DNA and histone modifications, etc [118][119][120]. Moreover, the declining costs of nucleotide sequencing, increasing data storage capacities and computer processing power sparks advances in bioinformatic analysis methods and mathematical modeling tools.…”

Arabidopsis bioinformatics: tools and strategies

“…The transcription factor binding database does not appear to contain DAP‐seq data. A review article by Kulkarni and Vandepoele (2020) on inferring gene regulatory networks provides a nice overview of six web tools for regulatory network inference, whereas Ko and Brandizzi (2020) dive deeper into the mechanics of using network‐based approaches for understanding gene regulation in plants.…”