Thousands of genomic, transcriptomic, methylomic, proteomic, and metabolomic experiments exist for model and commodity crops. Most researchers doing QTL mapping still rely solely on genetic associations and bring little of this big data to bear on further locus refinement and gene discovery. This is a failure of the crop bioinformatics community to create a coherent, searchable resources that are driven by a genome-to-phenotype paradigm.
The purpose of this Research Topic call is to explore new methods and resources by which a researcher can compile and use all relevant, publicly-available data associated their loci and trait of interest.
The topic welcomes Original Research and Reviews related to the digitizing plant breeding for cereal, horticultural, and other crop species. Example sub-topics include:
• Reducing redundant allele discovery in pathogen resistance mapping
• Connecting transcriptome data to discovered QTLs
• Integrating new genome assemblies into plant genetic analysis
• Interchangeable data formats and structures for ‘-omics’ resources
• Ontologies versus AI-driven feature detection
• Causal-variant/Functional-marker prediction
Thousands of genomic, transcriptomic, methylomic, proteomic, and metabolomic experiments exist for model and commodity crops. Most researchers doing QTL mapping still rely solely on genetic associations and bring little of this big data to bear on further locus refinement and gene discovery. This is a failure of the crop bioinformatics community to create a coherent, searchable resources that are driven by a genome-to-phenotype paradigm.
The purpose of this Research Topic call is to explore new methods and resources by which a researcher can compile and use all relevant, publicly-available data associated their loci and trait of interest.
The topic welcomes Original Research and Reviews related to the digitizing plant breeding for cereal, horticultural, and other crop species. Example sub-topics include:
• Reducing redundant allele discovery in pathogen resistance mapping
• Connecting transcriptome data to discovered QTLs
• Integrating new genome assemblies into plant genetic analysis
• Interchangeable data formats and structures for ‘-omics’ resources
• Ontologies versus AI-driven feature detection
• Causal-variant/Functional-marker prediction
