中华水蝎(异翅目:蝎科)染色体水平的基因组组装。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Xinzhi Liu, Ling Ma, Li Tian, Fan Song, Tongyin Xie, Yunfei Wu, Hu Li, Wanzhi Cai, Yuange Duan
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引用次数: 0

摘要

异翅目(真正的虫类)是昆虫中最多样化的类群之一,其摄食策略和生活习性多样化,因此成为研究适应性进化的理想类群。中国水蝎子(异翅目:蝎科)是一种生活在透水水系中的肉食性昆虫,是研究生境转换和水环境适应性的理想模型。然而,对这种水生昆虫的遗传研究仍然有限。在此,我们利用PacBio HiFi长读数和Hi-C测序读数获得了R. chinensis的染色体级基因组。基因组的总组装大小为 867.89 Mb,支架 N50 长度为 26.48 Mb,GC 含量为 39.50%。所有等位基因被组装成 23 个假染色体(N = 19 A + X1X2X3X4),我们预测该基因组中有 18,424 个编码蛋白质的基因。这项研究将为今后研究水蝽的生物学、水适应性和基因组进化提供宝贵的基因组资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Chromosome-level genome assembly of Chinese water Scorpion Ranatra chinensis (Heteroptera: Nepidae).

Heteroptera (the true bugs), one of the most diverse lineages of insects, diversified in feeding strategies and living habitats, and thus become an ideal lineage for studies on adaptive evolution. Chinese water scorpion Ranatra chinensis (Heteroptera: Nepidae) is a predaceous bug living in lentic water systems, representing an ideal model for studying habitat transition and adaptation to water environment. However, genetic studies on this water bug remain limited. Here, we obtained a chromosome-level genome of R. chinensis using PacBio HiFi long reads and Hi-C sequencing reads. The total assembly size of genome is 867.89 Mb, with a scaffold N50 length of 26.48 Mb and the GC content of 39.50%. All contigs were assembled into 23 pseudo-chromosomes (N = 19 A + X1X2X3X4), and we predicted 18,424 protein-coding genes in this genome. This study will provide valuable genomic resources for future studies on the biology, water adaptation, and genome evolution of water bugs.

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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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