Accelerating pulse breeding using machine learning. Advances in genomics and high throughput phenotyping are generating vast quantities of data that can be applied for crop improvement, however the lack of computational analysis tools and approaches limits the full exploitation of this data. Pulse legumes are currently under utilised in Australian agriculture due to poor adaptation, however they offer significant benefits both for soil improvement and the production of high protein crops. This p ....Accelerating pulse breeding using machine learning. Advances in genomics and high throughput phenotyping are generating vast quantities of data that can be applied for crop improvement, however the lack of computational analysis tools and approaches limits the full exploitation of this data. Pulse legumes are currently under utilised in Australian agriculture due to poor adaptation, however they offer significant benefits both for soil improvement and the production of high protein crops. This project will develop machine learning (ML) tools for the analysis of pulse legume crop traits and their association with genomic variation to accelerate the breeding of high performance pulse legumes for Australian growers.Read moreRead less
Who’s who in the plant gene world? As many more plant genomes are sequenced, the bottleneck is being able to interrogate and translate this data into applications for crop improvement. This project will develop and apply a population graph database, hosting genome data for the world’s major crops and their wild relatives, allowing the characterisation of gene diversity on an unparalleled scale. Analysis of this data will reveal the presence/absence and sequence diversity for classes of genes for ....Who’s who in the plant gene world? As many more plant genomes are sequenced, the bottleneck is being able to interrogate and translate this data into applications for crop improvement. This project will develop and apply a population graph database, hosting genome data for the world’s major crops and their wild relatives, allowing the characterisation of gene diversity on an unparalleled scale. Analysis of this data will reveal the presence/absence and sequence diversity for classes of genes for important agronomic traits including disease resistance, flowering time and legume nitrogen fixation which will enable plant breeders to identify and apply novel genes and allelic variants for use in breeding programmes, accelerating the production of improved crop varieties.Read moreRead less