The chromosome-level genome assembly and annotation of an invasive forest pest Obolodiplosis robiniae.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-11-14 DOI:10.1038/s41597-024-04037-x
Lan Huang, Li Wang, Hui-Quan Sun, Wen-Xia Huai, Ruo-Zhu Lin, Shu-Jun Wei, Yan-Xia Yao
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Abstract

Biological invasion is a major global problem, leading to the loss of biodiversity and species extinction, and causing huge economic losses to countries. Obolodiplosis robiniae is a major invasive forest pest that has caused economic losses in Asia and Europe. Here, the chromosome- level genome of O. robiniae was assembled using the PacBio platform and Hi-C technology. A contig-level genome with a length of 199.49 Mb and a contig N50 of 4.66 Mb was assembled. Approximately 98.05% of contigs were successfully anchored to four chromosomes using Hi-C assisted genome assembly. The genome integrity was assessed to be 90.3% based on BUSCOs analysis. The high-quality genome provides valuable data for the study of invasive species, and a foundation for the understanding the biology and ecology of O. robiniae.

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入侵森林害虫 Obolodiplosis robiniae 的染色体级基因组组装和注释。
生物入侵是一个重大的全球性问题,它导致生物多样性丧失和物种灭绝,并给各国造成巨大的经济损失。Robiniae 是一种主要的入侵性森林害虫,给亚洲和欧洲造成了经济损失。在这里,我们利用 PacBio 平台和 Hi-C 技术组装了 O. robiniae 的染色体级基因组。组装的等位基因长度为 199.49 Mb,等位基因 N50 为 4.66 Mb。利用 Hi-C 辅助基因组组装技术,约 98.05% 的等位基因成功锚定到四条染色体上。根据 BUSCOs 分析评估,基因组完整性为 90.3%。高质量的基因组为研究入侵物种提供了宝贵的数据,也为了解 O. robiniae 的生物学和生态学奠定了基础。
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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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