Chromosome-scale genome assembly and annotation of Huzhang (Reynoutria japonica).

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-03-21 DOI:10.1038/s41597-025-04773-8
Jumei Zhang, Qing Xu, Lei You, Bin Li, Zezhi Zhang, Wenyao Lin, Xiangyin Luo, Zhengxiu Ye, Lanlan Zheng, Chen Li, Junpeng Niu, Guodong Wang, Honghong Hu, Chao Zhou, Yonghong Zhang
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Abstract

Reynoutria japonica, commonly known as Huzhang or Japanese knotweed, is a perennial herbaceous plant belonging to the family Polygonaceae and order Caryophyllales. This plant is valued for its traditional medicinal uses in China. In this study, we present a high-quality, chromosome-scale reference assembly for R. japonica using a combination of PacBio long-read sequencing, Hi-C reads, and Illumina short-read sequencing. The final assembled genome spans approximately 3.30 Gb, with a contig N50 of 1.39 Mb. Notably, 99.22% of the assembled sequences were anchored to 22 pseudo-chromosomes, and 74.79% of the genome is composed of repetitive elements. Genome annotation revealed 68,646 protein-coding genes and 14,788 non-coding RNAs. This genomic resource provides a robust foundation for comparative genomics and will enable deep insights into the evolutionary relationships across related species.

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胡张(Reynoutria japonica)染色体级基因组组装与注释。
日本野樱草,俗称虎杖或日本虎杖,是蓼科石竹目多年生草本植物。这种植物因其在中国的传统药用价值而受到重视。在这项研究中,我们利用PacBio长读段测序、Hi-C测序和Illumina短读段测序的组合,建立了一个高质量的、染色体尺度的粳稻参考组合。最终组装的基因组全长约3.30 Gb, N50为1.39 Mb。值得注意的是,99.22%的组装序列锚定在22条伪染色体上,74.79%的基因组由重复元件组成。基因组注释显示68,646个蛋白质编码基因和14,788个非编码rna。这种基因组资源为比较基因组学提供了坚实的基础,并将使深入了解相关物种之间的进化关系成为可能。
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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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