Genome assembly of the grassland caterpillar Gynaephora qinghaiensis.

IF 6.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-01-27 DOI:10.1038/s41597-025-04466-2

Youpeng Lai, Shan Xiao, Minggang Qin, Xinhai Ye, Fang Wang, Qi Fang

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Abstract

The grassland caterpillars are the most damaging insect pests to the alpine meadow of the Qinghai-Tibetan Plateau in China. In this study, we present a genome assembly of one grassland caterpillar Gynaephora qinghaiensis by using Oxford Nanopore long-read and BGI short-read sequencing. The genome assembly of 861.04 Mb in size consists of 107 contigs, with a contig N50 size of 18.65 Mb. The BUSCO analysis revealed the presence of 99.56% (99.27% complete and 0.29% fragmented) BUSCO genes in the assembly. 580.2 Mb (67.4% of genome) of repetitive sequences and 16,618 protein-coding genes were predicted in G. qinghaiensis genome. Phylogenomic analysis indicated that G. qinghaiensis and the rusty tussock moth Orgyia antiqua diverged approximately 18.3 million years ago. Moreover, gene family evolution analysis suggested that 130 gene families significantly expanded and 43 contracted in the G. qinghaiensis genome. The availability of the reference genome could provide genetic resources to uncover adaptive evolutionary mechanisms of grassland caterpillars to high-altitude environments and contributes to the development of integrated pest management strategies.

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草地毛虫青海妇毛虫基因组组装。

草地毛虫是青藏高原高寒草甸危害最大的害虫。在这项研究中，我们利用牛津纳米孔长读测序和华大基因短读测序，对一种草地毛虫（gyphora qinghaiensis）进行了基因组组装。基因组组装体大小为861.04 Mb，由107个contigs组成，其中contig N50大小为18.65 Mb。BUSCO分析显示，该组合中存在99.56%（99.27%完整，0.29%片段化）的BUSCO基因。结果表明，青海海鸡基因组共包含580.2 Mb（67.4%）重复序列和16618个蛋白质编码基因。系统基因组学分析表明，青海蛾和古锈斑蛾大约在1830万年前发生分化。基因家族进化分析表明，青海海鸡基因组中有130个基因家族显著扩增，43个基因家族收缩。参考基因组的获得可为揭示草原毛虫对高海拔环境的适应性进化机制提供遗传资源，并有助于制定害虫综合治理策略。

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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.