Chromosome-level genome assembly of Phytoseiulus persimilis Athias-Henriot.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-18 DOI:10.1038/s41597-025-04631-7

Xinyuan Zhou, Endong Wang, Xuenong Xu, Bo Zhang

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Abstract

As a globally recognized predatory mite, Phytoseiulus persimilis Athias-Henriot is known for its highly effective control of pest mites. Also, as a model species in Phytoseiidae, P. persimilis possesses unique biological characteristics, such as the first offspring developing into a male with a strict sex sequence. However, the genetic mechanisms have not been fully unrevealed yet. To lay the groundwork for genetic research, we presented a high-quality chromosomal genome of P. persimilis with PacBio HiFi and Hi-C data. The total length of genome is 214.23 Mb, of which 190.48 (88.91%) is anchored on 4 chromosomes. The scaffold N50 is 57.95 Mb and the BUSCO (Benchmarking Universal Single-Copy Orthologs) completeness is 98.3%. Repeat elements comprise 27.59% (59.10 Mb) of the genome. The genome contained 15,847 predicted protein-coding genes, 12,344 of which were annotated for function. This high-quality genome of P. persimilis would allow us to explore the genetic mechanism underlying the biological characteristics of the Phytoseiidae species, and provide possibilities for the industrial optimization of commercial predatory mites in the future.

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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.