Computational aspects underlying genome to phenome analysis in plants.

Area of Science:

• Plant Science
• Genomics
• Phenotyping

Background:

• Genomics and high-throughput phenotyping are advancing plant science and breeding.
• Challenges remain in standardizing experiments and integrating data for sophisticated analyses.

Purpose of the Study:

• To review current genomics and pangenomics in plants.
• To highlight the importance of standardizing phenotyping data using MIAPPE.
• To explore linking phenotypic and genomic data using machine learning.

Main Methods:

• Review of genome assembly and pangenomics.
• Description of the Minimum Information About a Plant Phenotyping Experiment (MIAPPE) standard.
• Exploration of deep phenotypic data analysis and machine learning applications.

Main Results:

• Genomics and phenotyping accelerate quantitative trait locus (QTL) and gene identification.
• Standardized phenotyping data (MIAPPE) enables data reuse and integration.
• Deep phenotypic data can reveal novel trait-trait correlations.
• Machine learning shows potential for linking phenotypes to genomic features.

Conclusions:

• Standardization (MIAPPE) and robust experimental design are crucial for integrating plant genomic and phenotypic data.
• Machine learning offers powerful tools for future discoveries in plant science by connecting genotype to phenotype.