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Chromosome-scale genome assembly of Glycyrrhiza uralensis revealed metabolic gene cluster centred specialized metabolites biosynthesis.

Amit RaiHideki HirakawaMegha RaiYohei ShimizuKenta ShirasawaShinji KikuchiHikaru SekiMami YamazakiAtsushi ToyodaSachiko N IsobeToshiya MuranakaTakashi Hashimoto
Published in: DNA research : an international journal for rapid publication of reports on genes and genomes (2022)
A high-quality genome assembly is imperative to explore the evolutionary basis of characteristic attributes that define chemotype and provide essential resources for a molecular breeding strategy for enhanced production of medicinal metabolites. Here, using single-molecule high-fidelity (HiFi) sequencing reads, we report chromosome-scale genome assembly for Chinese licorice (Glycyrrhiza uralensis), a widely used herbal and natural medicine. The entire genome assembly was achieved in eight chromosomes, with contig and scaffold N50 as 36.02 and 60.2 Mb, respectively. With only 17 assembly gaps and half of the chromosomes having no or one assembly gap, the presented genome assembly is among the best plant genomes to date. Our results showed an advantage of using highly accurate long-read HiFi sequencing data for assembling a highly heterozygous genome including its complexed repeat content. Additionally, our analysis revealed that G. uralensis experienced a recent whole-genome duplication at approximately 59.02 million years ago post a gamma (γ) whole-genome triplication event, which contributed to its present chemotype features. The metabolic gene cluster analysis identified 355 gene clusters, which included the entire biosynthesis pathway of glycyrrhizin. The genome assembly and its annotations provide an essential resource for licorice improvement through molecular breeding and the discovery of valuable genes for engineering bioactive components and understanding the evolution of specialized metabolites biosynthesis.
Keyphrases
  • genome wide
  • single molecule
  • copy number
  • dna methylation
  • ms ms
  • single cell
  • gene expression
  • machine learning
  • atomic force microscopy
  • cell wall
  • genome wide analysis
  • fluorescent probe