A chromosome-level genome assembly of the heteronomous hyperparasitoid wasp Encarsia sophia.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-11-19 DOI:10.1038/s41597-024-04040-2

Xiaoming Man, Cong Huang, Shengyong Wu, Jianyang Guo, Fanghao Wan, Frédéric Francis, Nianwan Yang, Wanxue Liu

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Abstract

Encarsia sophia, a heteronomous hyperparasitoid wasp, is a well-known biological control agent, but its genomic information is limited, hindering molecular investigations and understanding of multitrophic interactions. In this study, we present a chromosome-level genome assembly for E. sophia using Illumina, PacBio HiFi, and Hi-C technologies. The assembled genome size is 398.3 Mb, with a contig N50 of 1.0 Mb and a scaffold N50 of 74.0 Mb. The BUSCO completeness score is 97.1%, and genome coverage reaches 99.1%. Utilizing Hi-C assisted assembly, the genome was organized into five chromosomes, with a mounting rate of 95.1%. Repetitive sequences make up 54.6% of the genome, and 14,914 protein-coding genes were predicted, with 95.5% functionally annotated. The high-quality genome assembly of E. sophia is a significant achievement, marking the first complete genome for a heteronomous hyperparasitoid wasp. This milestone offers valuable insights into the evolution and host interactions of heteronomous hyperparasitoids, laying the foundation for extensive research in biological control.

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异源超寄生蜂 Encarsia sophia 的染色体级基因组组装。

Encarsia sophia 是一种异源超寄生蜂，是一种著名的生物防治药剂，但其基因组信息有限，阻碍了分子研究和对多营养体相互作用的理解。在本研究中，我们利用 Illumina、PacBio HiFi 和 Hi-C 技术，对 E. sophia 进行了染色体组水平的基因组组装。组装的基因组大小为 398.3 Mb，等位基因 N50 为 1.0 Mb，支架 N50 为 74.0 Mb。BUSCO 完整性得分为 97.1%，基因组覆盖率达到 99.1%。利用 Hi-C 辅助组装技术，基因组被组织成五条染色体，安装率为 95.1%。重复序列占基因组的 54.6%，预测了 14,914 个编码蛋白质的基因，其中 95.5%的基因有功能注释。高质量的E. sophia基因组组装是一项重大成就，标志着第一个完整的异源超寄生蜂基因组的完成。这一里程碑式的成果为了解异源超寄生蜂的进化和寄主相互作用提供了宝贵的信息，为生物防治领域的广泛研究奠定了基础。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.