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Advances and prospects of orchid research and industrialization.

Di-Yang ZhangXue-Wei ZhaoYuan-Yuan LiShi-Jie KeWei-Lun YinSi-Ren LanZhong-Jian Liu
Published in: Horticulture research (2022)
Orchidaceae is one of the largest, most diverse families in angiosperms with significant ecological and economical values. Orchids have long fascinated scientists by their complex life histories, exquisite floral morphology and pollination syndromes that exhibit exclusive specializations, more than any other plants on Earth. These intrinsic factors together with human influences also make it a keystone group in biodiversity conservation. The advent of sequencing technologies and transgenic techniques represents a quantum leap in orchid research, enabling molecular approaches to be employed to resolve the historically interesting puzzles in orchid basic and applied biology. To date, 16 different orchid genomes covering four subfamilies (Apostasioideae, Vanilloideae, Epidendroideae, and Orchidoideae) have been released. These genome projects have given rise to massive data that greatly empowers the studies pertaining to key innovations and evolutionary mechanisms for the breadth of orchid species. The extensive exploration of transcriptomics, comparative genomics, and recent advances in gene engineering have linked important traits of orchids with a multiplicity of gene families and their regulating networks, providing great potential for genetic enhancement and improvement. In this review, we summarize the progress and achievement in fundamental research and industrialized application of orchids with a particular focus on molecular tools, and make future prospects of orchid molecular breeding and post-genomic research, providing a comprehensive assemblage of state of the art knowledge in orchid research and industrialization.
Keyphrases
  • genome wide
  • copy number
  • single cell
  • current status
  • healthcare
  • dna methylation
  • endothelial cells
  • electronic health record
  • risk assessment
  • molecular dynamics
  • machine learning
  • artificial intelligence
  • data analysis