Plant Cis-Regulatory Elements: Prediction, Identification and Characterization

A central goal in biology is to understand how a limited number of transcription factors (TFs) drive dynamic gene expression changes in different cell types and environmental conditions. TFs recognize specific cis-regulatory elements (CREs) to achieve precise spatial-temporal control of target gene expression. In recent years, our ability to predict, identify, and validate the regulatory regions has greatly accelerated, underpinned by advances in biotechnology (genome sequence, chromatin profiling) and information technology (machine learning). Promising results have been made to link cis-regulatory variation with gene expression and plant phenotype. Functionally characterizing CREs can thus provide important insight into gene regulation mechanisms, enable better identification of causal genetic variants and interpretation of their effects and, finally, enable smart design of future crops.

A challenge to the identification of plant cis-regulatory elements concerns the limited availability of high-quality genomic sequences, especially in the repeat-rich intergenic regions. This is also a significant gap in building the infrastructure for functional plant research: more public projects like the human ENCODE and ROADMAP are eagerly called for. Finally, there has been limited attempt to apply new techniques (both computationally and experimentally) to accelerate the search and characterization of functional CREs in plants.

In this Research Topic, we welcome all article types published by Frontiers in Plant Science that concern the prediction, identification, and characterization of plant CREs especially those that focus on:

• Systematic identification of functional CREs through experimental approaches such as ChIP-seq, DAP-seq, CUT&Tag, DNase-seq, ATAC-seq, MOA-seq, etc.

• Machine learning mining, modeling, and prediction of plant CREs using omics data;

• Dissection of cis- and trans- regulatory patterns using allele-specific analysis in hybrids, or identification of functional cis-regulatory variants through eQTL, eQTL, mQTL or pQTL analysis using diversity panels;

• Functional characterization of cis-regulatory variants using mutagenesis, high-throughput techniques such as PBMs, parallel CRISPR, MPRA and STARR-seq.

Keywords: cis-regulatory elements, open chromatin, eQTL, STARR-seq, data mining, machine learning

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.