This Research Topic is part of the article collection series - Multi-omics and Computational Biology in Horticultural Plants: From Genotype to Phenotype
Horticultural plants play an important role for humans by providing herbal medicines, beverages, vegetables, fruits, and ornamentals. High-throughput technologies have revolutionised the time scale and power of detecting insights into physiological changes and biological mechanisms in plants. All sequencing data and tools have helped us better understand the evolutionary histories of horticultural plants and provide genotype and phenotype resources for molecular studies on economically important traits. The integration of these -omics technologies (e.g., genomics, transcriptomics, proteomics, metabolomics, lipidomics, ionomics, and redoxomics) is currently at the forefront of plant research.
The genomes of horticultural plants are highly diverse and complex, often with a high degree of heterozygosity and polyploidy. Novel computational methods need to be developed to take advantage of state-of-the-art genomic technologies. As a result, the mining of multi-omics data and the development of new computational biology approaches for the reliable and efficient analysis of plant traits is necessary.
Multi-omics and computational biology approaches allow understanding the biochemical mechanisms in plant traits. The integration of multi-omics data and computational biology technologies constructs biological networks to identify traits that can be further applied toward horticultural crops breeding and more productive crop varieties.
This Research Topic aims to combine high-throughput omics and computational biology technologies to find a coherently matching geno-pheno relationship or association in horticultural crops research. We encourage manuscripts dedicated to improving our understanding of biological mechanisms from genotype to phenotype.
We welcome submissions of various article types, including original research papers, reviews, minireviews, methods, perspectives, etc., on the following sub-themes but not limited to:
• Computational methods or machine learning approaches for modelling biological processes.
• Discovering geno-pheno associations.
• Gene-gene interactions and gene-environment interactions for economically important traits association analysis.
• New computational methods for gene expression data analysis.
• Machine learning approaches for modelling gene regulatory networks.
• Identification of expression patterns.
• Reviews of computational methods for gene expression data analysis.