There is a rapid evolution of high-throughput omics technologies in plant research field, which include genomics, transcriptomics, epigenomics, proteomics, metabolomics and others. These methods have produced vast amount of data that requires efficient development and use of computational tools for integration, analysis and interpretation. High-throughput investigations into plant development, combined with advances in computational approaches, have significantly enhanced our understanding of complex plant systems and led to groundbreaking discoveries in plant development and physiology.

Recently, cutting-edge techniques such as long-read genome sequencing, single-cell/nuclei RNA sequencing, spatial transcriptomics, and proximity labeling-based methods have enabled plant researchers to identify novel regulators of various plant developmental processes. These techniques provide deep spatiotemporal resolution in characterizing plant developmental processes at all molecular levels. However, extracting key insights from the large-scale data and enhancing data accessibility remain significant challenges. Unlike confocal imaging, which directly represents plant morphology and physiological development, linking high-throughput data to its biological meanings can be difficult. Advances in computational tools, especially those with more standardized output formats, more understandable codes and parameters, and easier-to-use visualization functions, can help to solve this problem. Also, as more high-throughput data is generated, integrating data from various omics platforms to construct comprehensive molecular interaction networks is the next step. Exploring correlations between data from different omics platforms with advanced bioinformatic tools can be a worthwhile direction.

Furthermore, the development of Artificial Intelligence (AI) techniques has enabled the use of machine learning algorithms for data annotation, key component characterization, genetic regulatory network construction, and phenotype predictions. Automated analysis models can facilitate the annotation and extraction of information from generated data, while predictive models can guide experimental design and hypothesis testing, thereby accelerating the discovery process in plant physiology research.

Overall, the development of omics technologies and computational tools has the potential to revolutionize our research on plant morphogenesis. By providing deeper insights into the molecular and cellular basis of plant development, these approaches can drive the breeding of crops that are more resilient to environmental challenges. In the context of climate change, the application of high-throughput omics studies, coupled with the development of computational tools, will contribute to globally more sustainable agriculture.

The rapid development of high-throughput omics technologies and bioinformatic methods has dramatically advanced our understanding of complex plant development. Our special issue seeks to bring together cutting-edge research that explores plant developmental processes through the integration of computational biology and omics studies, whether through integrated multi-omics approaches or focused single-omics studies.

We invite researchers to submit original research articles, reviews, and short communications covering, but not limited to, the following topics:

- Genomic and Epigenomic Studies: Uncovering novel genes and cis-elements controlling plant development patterns.

- Single-cell or bulk transcriptomic Analyses: Elucidating gene expression patterns and the regulatory networks governing plant cell differentiation and organ morphogenesis.

- Proteomic Profiling: Identifying protein complexes controlling plant cell behaviors and organ growth.

- Metabolomic Investigations: Exploring plant metabolic pathways and their responses to environmental stimuli.

- Integrative Multi-Omics Approaches: Providing a comprehensive understanding of plant developmental process through the integration of multiple omics datasets.

- Computational Tools and Methodologies: Developing and applying innovative tools for omics data analysis and integration.

We welcome method papers utilizing published data, primary research involving data generation and analysis, as well as reviews that summarize advancements in multi-omics studies. We especially encourage submissions that highlight the development of computational tools for high-throughput plant studies and the integration of data from multiple omics platforms.

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Frontiers in Plant Physiology

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