A chromosomal-level genome assembly of Trichogramma chilonis Ishii, 1941 (Hymenoptera: Trichogrammatidae).

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-03-19 DOI:10.1038/s41597-025-04792-5

Chengxing Wang, Zhenjuan Yin, Yan Liu, Xiaoyan Dai, Shan Zhao, Ruijuan Wang, Yu Wang, Long Su, Hao Chen, Li Zheng, Yifan Zhai

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引用次数: 0

Abstract

Trichogramma spp. is a genus of minute egg parasitoids frequently used in agricultural pest management that can feed on the eggs of various lepidopteran pests. Currently, there is a scarcity of high-quality genomic resources for this category of tiny parasitoids, which impedes our comprehension of the population evolution and parasitic ecology of this collective. In this case, a chromosome-level genome of Trichogramma chilonis was produced by integrating PacBio HiFi, Illumina, and Hi-C data. The genome size totals 202.48 Mb, with a scaffold N50 length of 40.00 Mb. A total of 98.59% (199.63 Mb) of contigs were effectively mapped onto five chromosomes. The BUSCO assessment revealed that the genome assembly achieved 98.1% (n = 1,367) completeness, with 95% representing single-copy BUSCOs and 3.1% duplicated BUSCOs. Also, the genome comprises 24.16% (48.91 Mb) repeat elements and 12,163 predicted protein-coding genes. The high-quality genome of T. chilonis presented in this study offers an invaluable asset for elucidating its evolutionary path and ecological interactions.

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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.