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Genome structure and content of the rice root-knot nematode (Meloidogyne graminicola).

Ngan Thi PhanJulie OrjuelaEtienne G J DanchinChristophe KloppLaetitia Perfus-BarbeochDjampa K L KozlowskiGeorgios D KoutsovoulosCéline Lopez-RoquesOlivier BouchezMargot ZahmGuillaume BesnardStéphane Bellafiore
Published in: Ecology and evolution (2020)
Discovered in the 1960s, Meloidogyne graminicola is a root-knot nematode species considered as a major threat to rice production. Yet, its origin, genomic structure, and intraspecific diversity are poorly understood. So far, such studies have been limited by the unavailability of a sufficiently complete and well-assembled genome. In this study, using a combination of Oxford Nanopore Technologies and Illumina sequencing data, we generated a highly contiguous reference genome (283 scaffolds with an N50 length of 294 kb, totaling 41.5 Mb). The completeness scores of our assembly are among the highest currently published for Meloidogyne genomes. We predicted 10,284 protein-coding genes spanning 75.5% of the genome. Among them, 67 are identified as possibly originating from horizontal gene transfers (mostly from bacteria), which supposedly contribute to nematode infection, nutrient processing, and plant defense manipulation. Besides, we detected 575 canonical transposable elements (TEs) belonging to seven orders and spanning 2.61% of the genome. These TEs might promote genomic plasticity putatively related to the evolution of M. graminicola parasitism. This high-quality genome assembly constitutes a major improvement regarding previously available versions and represents a valuable molecular resource for future phylogenomic studies of Meloidogyne species. In particular, this will foster comparative genomic studies to trace back the evolutionary history of M. graminicola and its closest relatives.
Keyphrases
  • genome wide
  • copy number
  • dna methylation
  • randomized controlled trial
  • gene expression
  • case control
  • systematic review
  • single cell
  • small molecule
  • transcription factor
  • heavy metals
  • deep learning
  • current status