HUBEI AGRICULTURAL SCIENCES ›› 2025, Vol. 64 ›› Issue (8): 152-159.doi: 10.14088/j.cnki.issn0439-8114.2025.08.023

• Medicinal Plant • Previous Articles     Next Articles

Biological characteristics and whole genome sequencing analysis of Naematelia aurantialba T-1

TIAN Shuang, BI Yu, GUO Yan, WU Hao-qiong, YE Yang, LIU Jia-ning, ZHU Jia-nan   

  1. Institute of Microbiology, Heilongjiang Academy of Sciences, Harbin 150010,China
  • Received:2025-03-21 Online:2025-08-25 Published:2025-09-12

Abstract: The spores of Naematelia aurantialba T-1 were used as the experimental material. The biological characteristics of Naematelia aurantialba were studied, and its whole genome was sequenced and analyzed. The results indicated that the optimal carbon source for T-1 was sucrose, the optimal nitrogen source was peptone, the optimal inorganic salt was potassium chloride, the most suitable pH was 7, and the optimal inoculation amount was 108 cells/mL. The sequencing quality of the genomic bases was between 35 and 40, which was relatively good. The AT and GC base contents were relatively uniform, and the sequencing results were normal. Through K-mer frequency analysis, the size of the T-1 genome was determined to be 26 612 159 bp. After the sequencing data were assembled, it was shown that the N50 value was relatively high, and the GC content was 56.45%. The display of the genome using Circos software indicated that the full-length of the T-1 genome was 18 180 385 base pairs, with a GC content of 56.46%. The genome was divided into 10 contigs. The phylogenetic tree showed that T-1 was most closely clustered with Naematelia aurantialba strains. The GO function classification was annotated into three major categories, among which the genes involved in biological processes were the most. After comparison with the KEGG database, a total of 4 096 genes were annotated into five major metabolic pathways, among which the genes involved in metabolic pathways were the most.

Key words: Naematelia aurantialba, biological characteristics, whole genome sequencing, functional prediction, pathway enrichment analysis

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