Chromosome-level genome assembly of the crofton weed (Ageratina adenophora).

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-04-02 DOI:10.1038/s41597-025-04637-1

Fuyan Liu, Lin Du, Tingting Li, Bo Liu, Jianyang Guo, Guifen Zhang, Yibo Zhang, Wanxue Liu, Yan Pan, Ying Zhang, Hailing Wang, Ruiying Li, Weining Song, Fanghao Wan

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引用次数: 0

Abstract

Crofton weed (Ageratina adenophora), a significant invasive species, extensively disrupts ecosystem stability, leading to considerable economic losses. However, genetic insights into its invasive mechanisms have been limited by a lack of genomic data. In this study, we present the successful de novo assembly of the triploid genome of A. adenophora, leveraging long-read PacBio Sequel, optical mapping, and Hi-C sequencing. Our assembly resolved into a haplotype-resolved genome comprising 51 chromosomes, with a total size of ~3.82 Gb and a scaffold N50 of 70.8 Mb. BUSCO analysis confirmed the completeness of 97.71% of genes. Genome annotation revealed 3.16 Gb (76.44%) of repetitive sequences and predicted 123,134 protein-coding genes, with 99.03% functionally annotated. The high-quality reference genome will provide valuable genomic resources for future studies on the evolutionary dynamics and invasive adaptations of A. adenophora.

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克罗夫顿杂草（Ageratina adenophora）是一种重要的入侵物种，它广泛破坏生态系统的稳定性，导致巨大的经济损失。然而，由于缺乏基因组数据，对其入侵机制的遗传学研究一直受到限制。在本研究中，我们利用长线程 PacBio Sequel、光学图谱和 Hi-C 测序技术，成功地从头组装了金龟子的三倍体基因组。我们的组装解析出了一个由 51 条染色体组成的单倍型解析基因组，总大小约为 3.82 Gb，支架 N50 为 70.8 Mb。BUSCO 分析确认了 97.71% 的基因的完整性。基因组注释揭示了 3.16 Gb（76.44%）的重复序列，预测了 123,134 个编码蛋白质的基因，其中 99.03% 的基因有功能注释。高质量的参考基因组将为今后研究A. adenophora的进化动态和入侵适应性提供宝贵的基因组资源。

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来源期刊

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.