IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Qian Zhao, Zixu Fan, Hui Yu, Zhanli Wang
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引用次数: 0

摘要

红豆杉(Dracocephalum rupestre Hance)是唇形科植物红豆杉属的中国传统中药材,具有消炎、抗病毒和抗肿瘤等多种保健功效。然而,龙脑属植物目前还没有参考基因组,这限制了对龙脑的育种、栽培和药用价值的研究。因此,我们结合 Pacbio HiFi 测序技术和 Hi-C 支架技术,对 D. rupestre 进行了染色体组水平的高质量基因组组装。最终的基因组为 435.45 Mb,等位基因 N50 为 49.83 Mb,支架 N50 为 59.06 Mb。组装序列锚定在 7 条染色体上,整合效率为 96.96%。此外,我们还预测了 25,865 个编码蛋白质的基因,其中 98.23% 的基因得到了功能注释。这些结果为了解 D. rupestre 独特表型的遗传基础提供了宝贵的资源,并将促进对该物种功能基因组学的进一步研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The high-quality chromosome-level genome assembly of Dracocephalum rupestre Hance.

Dracocephalum rupestre Hance is China's traditional herbal medicine in the family Labiatae with numerous health benefits, including anti-inflammatory, antiviral and anti-tumor activities. However, the genus Dracocephalum has no reference genome currently, which restricts the research on the breeding, cultivation and exploration of medicinal properties in D. rupestre. Thus, we present the high-quality chromosome-level genome assembly of D. rupestre using a combination of Pacbio HiFi sequencing and Hi-C scaffolding technologies. The final genome was 435.45 Mb with a contig N50 of 49.83 Mb and a scaffold N50 of 59.06 Mb. The assembled sequences were anchored to 7 chromosomes with an integration efficiency of 96.96%. Furthermore, we predicted 25,865 protein-coding genes, 98.23% of which were functionally annotated. These results offer valuable resources for understanding the genetic basis of the unique phenotypes of D. rupestre and will facilitate further study of the functional genomics of this species.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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