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PI-Plat: a high-resolution image-based 3D reconstruction method to estimate growth dynamics of rice inflorescence traits.

Jaspreet SandhuFeiyu ZhuPuneet PaulTian GaoBalpreet K DhattYufeng GePaul StaswickHongfeng YuHarkamal Walia
Published in: Plant methods (2019)
For harnessing the potential of the existing genetic resources, we need a comprehensive understanding of the genotype-to-phenotype relationship. Relatively low-cost sequencing platforms have facilitated high-throughput genotyping, while phenotyping, especially for complex traits, has posed major challenges for crop improvement. PI-Plat offers a low cost and high-resolution platform to phenotype inflorescence-related traits using 3D reconstruction-based approach. Further, the non-destructive nature of the platform facilitates analyses of the same panicle at multiple developmental time points, which can be utilized to explore the genetic variation for dynamic inflorescence traits in cereals.
Keyphrases
  • low cost
  • high throughput
  • genome wide
  • high resolution
  • single cell
  • dna methylation
  • copy number
  • machine learning
  • risk assessment
  • deep learning
  • human health
  • big data
  • high speed