Chromosome-level genome assembly of Megachile lagopoda (Linnaeus, 1761) (Hymenoptera: Megachilidae).

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2024-10-29 DOI:10.1038/s41597-024-04028-y

Dan Zhang, Jianfeng Jin, Zeqing Niu, Michael C Orr, Feng Zhang, Rafael R Ferrari, Qingtao Wu, Qingsong Zhou, Wa Da, Arong Luo, Chaodong Zhu

引用次数: 0

Abstract

Megachile is one of the largest bee genera, including nearly 1,500 species, but very few chromosome-level assemblies exist for this group or the family Megachilidae. Here, we report the chromosome-level genome assembly of Megachile lagopoda collected from Xizang, China. Using PacBio CLR long reads and Hi-C data, we assembled a genome of 256.83 Mb with 96.08% of the assembly located on 16 chromosomes. Our assembly contains 266 scaffolds, with a scaffold N50 length of 15.6 Mb, and BUSCO completeness of 99.20%. We masked 27.10% (69.61 Mb) of the assembly as repetitive elements, identified 459 non-coding RNAs, and predicted 11,157 protein-coding genes. This high-quality genome of M. lagopoda represents an important step forward for our knowledge of megachilid genomics and bee evolution overall.

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Megachile lagopoda (Linnaeus, 1761) (Hymenoptera: Megachilidae) 染色体级基因组组装。

Megachile是最大的蜂属之一，包括近1,500个物种，但该蜂属或Megachilidae科的染色体组组装却很少。在这里，我们报告了从中国西藏采集的Megachile lagopoda的染色体组水平基因组组装。利用 PacBio CLR 长读数和 Hi-C 数据，我们组装了一个 256.83 Mb 的基因组，其中 96.08% 的基因组位于 16 条染色体上。我们的装配包含 266 个支架，支架 N50 长度为 15.6 Mb，BUSCO 完整性为 99.20%。我们屏蔽了 27.10% （69.61 Mb）的重复元件，鉴定了 459 个非编码 RNA，并预测了 11,157 个蛋白质编码基因。这个高质量的M. lagopoda基因组代表着我们在巨蜂基因组学和蜜蜂进化方面迈出了重要的一步。

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.