A high-quality chromosome-level genome assembly of the mulberry looper, Phthonandria atrilineata.

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
De-Long Guan, Ying-Can Qin, Ya-Zhen Chen, Shi-Hao Zhang, Ji-Ping Liu, Hui-Yu Yi, Xiao-Dong Li
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引用次数: 0

Abstract

The mulberry looper (Phthonandria atrilineata), a geometrid moth, plays a pivotal role in the destruction of mulberry trees (Morus spp.). In China, P. atrilineata is the most significant insect pest to sericulture, as it feeds on mulberry leaves and spreads diseases. The outbreak trend of P. atrilineata has been expanding yearly, causing substantial economic losses. Despite its ecological and economic importance, knowledge about the genomic background of P. atrilineata remains limited. Here, we report a chromosome-level reference genome of P. atrilineata, with a total size of 336.55 Mb, containing 15,026 protein-coding genes and 39.72% repeat sequences. These findings have the potential to shed light on the genetic basis of the destructive nature and environmental adaptation of P. atrilineata, offering valuable genomic resources for understanding genome evolution and pest management within this Lepidopteran pest.

桑树环虫Phthonandria atrilineata的高质量染色体水平基因组组装。
桑线虫(Phthonandria atrilineata)是一种尺蠖蛾,在桑树(Morus spp.)的毁坏中起着关键作用。在中国,桑环纹夜蛾是蚕桑业最主要的害虫,它以桑叶为食并传播疾病。该虫的爆发趋势逐年扩大,造成了巨大的经济损失。尽管 P. atrilineata 在生态和经济方面具有重要意义,但人们对其基因组背景的了解仍然有限。在此,我们报告了一个染色体水平的 P. atrilineata 参考基因组,总大小为 336.55 Mb,包含 15,026 个蛋白编码基因和 39.72% 的重复序列。这些发现有望揭示 P. atrilineata 的破坏性和环境适应性的遗传基础,为了解这种鳞翅目害虫的基因组进化和害虫管理提供宝贵的基因组资源。
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来源期刊
Scientific Data
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.
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