Modern cereal breeding and crop improvement rely on genotypic and phenotypic data to identify the important genetics that drive crop performance such as high yield, salinity, drought or heat tolerance.
The OzBarley genotype-to-phenotype (G2P) data asset will make this data available for the discovery of important genes, reducing the time and cost of breeding new barley varieties.
The OzBarley project is developing a publicly available Genotype-to-Phenotype (G2P) data asset meeting FAIR principles that is specifically designed by, and for, Australian researchers and breeders focusing on barley as an economically important model crop.
The project involves the following elements:
- audit of existing datasets to understand data formats, metadata standards, existing ontologies, annotations, and assessment of datasets against the FAIR principles
- quality control and curation of individual datasets focussing on interoperability
- development of a G2P data model to create formal linkage between genomic and phenomic data
- delivery of enhanced combined data assets that are findable and accessible.
Who Will Benefit
Researchers and research organisations, plant breeders, Australian grains industry, data managers and analysts will benefit from the project’s core features:
- collaboration – public/private collaboration to ensure open and integrated data access to academic and industry users alike, and a foundation for researchers to grow the data asset in the future.
- integrated genotypic and/or phenotypic data – providing FAIR data standards for phenotypic data sets in future projects maximising value from the complementary G2P data asset and ensuring community uptake.
Our partners are:
- Australian Plant Phenomics Facility (APPF)
- Bioplatforms Australia (BPA).
Our collaborating organisations are:
- Australian Grain Technologies
- Secobr Researchers
- University of Adelaide
- Federation University.
OzBarley will create a G2P data asset for the assessment of crop genomes, and the corresponding variability in trait expression with relation to the environment. This will allow researchers, breeders, bioinformaticians and machine learning experts to jointly work towards extracting maximum value from the data to support crop improvement.
Public/private collaboration will ensure open and integrated data access to academic and industry users, and a foundation for researchers to grow the data asset in the future.