花斑蝶 Nilaparvata muiri 染色体级基因组组装。

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-09-19 DOI:10.1038/s41597-024-03870-4

Cilin Wang, Ju Luo, Aiying Wang, Guiying Yang, Jian Tang, Shuhua Liu

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引用次数: 0

摘要

Nilaparvata muiri（半翅目：Delphacidae）是一种毁灭性水稻害虫--褐飞虱 Nilaparvata lugens 的同胞种。在这里，我们结合使用了 PacBio HiFi 测序、Illumina 短线程测序和 Hi-C 支架技术，生成了 N. muiri 的高质量染色体组。基因组组装（524.9 Mb）锚定在 15 个假染色体上，支架 N50 为 43.3 Mb，BUSCO 完整性为 99.1%。它包含 188.1 Mb 的重复序列和 13204 个编码蛋白质的基因。作为与重要害虫 N. lugens 同源的近缘种，N. muiri 染色体水平的基因组组装将为基于比较基因组学更好地分析 N. lugens 的致病机制提供重要支持。

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Chromosome-level genome assembly of the planthopper Nilaparvata muiri.

The Nilaparvata muiri (Hemiptera: Delphacidae) is a sibling species of a destructive rice insect pest, the brown planthopper (BPH), Nilaparvata lugens. Here, we generated a high-quality chromosome-level genome assembly of N. muiri using a combination of the PacBio HiFi sequencing, Illumina short-read sequencing and Hi-C scaffolding technologies. The genome assembly (524.9 Mb) is anchored to 15 pseudochromosomes, with a scaffold N50 of 43.3 Mb and 99.1% BUSCO completeness. It contains 188.1 Mb repeat sequences and 13204 protein-coding genes. As a closely related species within the same genus as the significant pest, N. lugens, the chromosome-level genome assembly of N. muiri will provide important support for the better analysis of pathogenicity mechanisms of N. lugens based on comparative genomics.

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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.