摘要
The abilities of plant biologists and breeders to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation,and particularly to collect this data at a high-throughput scale with low cost.Although deep learning methods have demonstrated unprecedented potential to automate plant phenotyping,these methods commonly rely on large training sets that can be time-consuming to generate.
基金
Poplar SNP dataset:Support for the Poplar GWAS data-set is provided by the U.S.Department of Energy,Office of Science Biological and Environmental Research(BER)via the Bioenergy Science Center(BESC)under Contract No.DE-PS02-06ER64304
The Poplar GWAS Project used resources of the Oak Ridge Leadership Computing Facility and the Compute and Data Environment for Science at Oak Ridge National Laboratory,which is supported by the Office of Science of the U.S.Department of Energy under Contract No.DE-AC05-00OR22725
We thank the National Science Foundation Plant Genome Research Program for funding“Analysis of Genes Affecting Plant Regeneration and Transformation in Poplar,”IOS#1546900,NSF IIS#1911232,and IIS#1909038
The research was also partially supported by Amazon Research Award,DARPA Contract N66001-19-2-4035,HR001120C0011 and a gift from Kuaishou Inc..
