Full-length transcriptome sequencing and dormancy gene mining of Paris polyphylla var. yunnanensis seeds
Objective To enrich the genome information database of Paris polyphylla var.yunnanensis and provide basis for its development and utilization.Methods The seeds of P.polyphylla in different germination stages were harvested,and the genome of P.polyphylla was sequenced by PacBio Sequel platform.Results Three generations of full-length transcriptome sequencing were performed on the mixed samples of P.polyphylla seeds at different germination stages,and a total of 496 536 polymerase readings were obtained.Isoform clustering results in 18 984 items,of which 17 196 items are annotated.NR annotation found that it had a gene matching ratio of 23.83%with Elaeis guineensis.GO annotation found that the genes of P.polyphylla seeds were mainly enriched in the metabolic process of cell components;Through KEGG annotation,it is known that the genes involved in metabolism of P.polyphylla seeds are dominant;KOG annotation found that the function of seed protein of P.polyphylla mainly focused on general function prediction.The annotation of TF transcription factors shows that the bZIP family(70)has the most transcription factors,among which 15 genes may be related to seed dormancy of P.polyphylla,in addition,KEGG annotated 8 WRKY genes and 9 IAA genes,which can be used as a scientific basis for further clarifying the seed.dormancy mechanism of P.polyphylla and deeply utilizing the gene resources of P.polyphylla.Conclusion The sequence and structural information of full-length transcripts of P.polyphylla seeds obtained by sequencing three generations of full-length transcriptome will help to reveal the dormancy mechanism of P.polyphylla seeds,lay a certain foundation for the next study of P.polyphylla,and also provide rel-evant molecular biological basis for the further development and utilization of Chinese herbal medicines.
Seeds of Paris polyphylla Smith var. yunnanensis(Franch.)Hand.-Mazz.Full-length transcriptomeFunctional notesGene mining