Medicinal and horticultural plants play multiple roles in our lives by providing sources of herbal medicines, beverages, vegetables, fruits, and as ornamental plants. In the last few years, high-throughput technologies have revolutionized the time scale and power of detection of physiological changes and biological mechanisms in plants. Indeed, current sequencing data and tools have helped us better understand their evolutionary history and provide genotype resources for molecular insight into economically important traits. The integration of multi-omics technologies (e.g., genomics, transcriptomics, proteomics, metabolomics, lipidomics, ionomics, and redoxomics) is currently at the forefront of plant research. The mining of multi-omics datasets and the development of new computational biology approaches for the reliable and efficient analysis of plant traits are necessary.
The `Plant data notes´ Collection aims to combine high-throughput omics and computational biology technologies to find a coherently matching geno-pheno association in medicinal and horticultural plant research. We encourage papers dedicated to improving our understanding of biological mechanisms or related data resources. Research articles, Data notes, and Database articles will be considered on the following topics, but not limited to:
- Bioinformatics methods or machine learning approaches for modeling biological processes
- Gene-environment interactions for economically important traits in plants
- QTL mapping and genome-wide association studies in plants
- Germplasm characterization using molecular and genomics techniques
- New computational methods for gene expression data analysis
- Bioinformatics approaches for modeling gene regulatory networks
- Identification of important gene families in plant stress biology
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