Chromosome-level genome assembly of the aphid Megoura crassicauda.

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Tianyu Huang, Xu Cheng, Bing Wang, Guirong Wang
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引用次数: 0

Abstract

Megoura crassicauda (Hemiptera: Aphididae) is a major pest species that inflicts significant damage on various legume crops worldwide, causing substantial global economic losses. In this study, we present a chromosome-scale genome assembly of M. crassicauda. By integrating PacBio long-read sequencing, Illumina short-read sequencing, and Hi-C scaffolding techniques, we constructed a genome assembly spanning 424.45 Mb genome. Approximately 93.74% of the assembly was successfully anchored into five scaffolds, with contig and scaffold N50 reaching 5.55 Mb and 103.55 Mb, respectively. The genome completeness, as evaluated by BUSCO, achieved a completeness score of 97.75%. Additionally, a total of 14,717 protein-coding genes were identified. This high-quality genome assembly of M. crassicauda serves as a valuable genomic resource, facilitating further studies into the ecological adaptations of M. crassicauda and the development of pest control strategies.

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蚜虫的染色体水平基因组组装。
扁蚜(半翅目:蚜虫科)是世界范围内对多种豆科作物造成严重危害的主要害虫,在全球范围内造成重大经济损失。在这项研究中,我们提出了一个染色体尺度的基因组组装。通过整合PacBio长读段测序、Illumina短读段测序和Hi-C脚手架技术,我们构建了一个424.45 Mb的基因组组装。约93.74%的组装成功锚定在5个支架中,其中contig和scaffold N50分别达到5.55 Mb和103.55 Mb。BUSCO评估的基因组完整性评分为97.75%。此外,共鉴定出14,717个蛋白质编码基因。这一高质量的稻蛾基因组组合为进一步研究稻蛾的生态适应性和制定害虫防治策略提供了宝贵的基因组资源。
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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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