Transcriptional regulation in plants: Using omics data to crack the cis-regulatory code

Abstract

Innovative omics technologies, advanced bioinformatics, and machine learning methods are rapidly becoming integral tools for plant functional genomics, with tremendous recent advances made in this field. In transcriptional regulation, an initial lag in the accumulation of plant omics data relative to that of animals stimulated the development of computational methods capable of extracting maximum information from the available data sets. Recent comprehensive studies of transcription factor-binding profiles in Arabidopsis and maize and the accumulation of uniformly processed omics data in public databases have brought plant biologists into the big leagues, with many cutting-edge methods available. Here, we summarize the state-of-the-art bioinformatics approaches used to predict or infer the cis-regulatory code behind transcriptional gene regulation, focusing on their plant research applications.

Keywords: ATAC-seq; Binding site; Chromatin; Cis-regulatory syntax; Composite cis-element; Epigenome; Integrative analysis; Machine learning (ML); Multiomics; Single-cell RNA-seq; Transcription factor; Transcriptome.