Chromosome-level genome assembly for three geographical stocks of large yellow croaker (Larimichthys crocea).

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2024-12-18 DOI:10.1038/s41597-024-04126-x

Xintong Chen, Lingwei Miao, Qian He, Qiaozhen Ke, Fei Pu, Ning Li, Tao Zhou, Peng Xu

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Abstract

Large yellow croaker (Larimichthys crocea) has been demonstrated to be divided into three geographical stocks from south to north along the coast of China, including Nanhai, Mindong, and Daiqu. Although multiple versions of L. crocea have been published, no high-quality Nanhai and Daiqu genomes have been assembled, hampering the assessment of the fine-scale genetic structure and adversely affecting wild stock conservation, fishery management, and germplasm exploitation of large yellow croaker. To fill the gap, we sequenced the genomes of three L. crocea stocks using a combination of PacBio and Hi-C technologies. We assembled each genome (~712 Mb) into 24 chromosomes with a contig N50 of 19.46-29.71 Mb and an integration efficiency of 88.13-92.80%. Furthermore, 26,851-28,133 protein-coding genes were predicted. The reference genomes of three geographical stocks of L. crocea provide vital resources for future research on the conservation and utilization of genetic diversity.

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大黄鱼三个地理种群的染色体水平基因组组装。

大黄鱼（Larimichthys crocea）在中国沿海从南到北分为南海、闽东和岱区三个地理种群。虽然大黄鱼的多个版本已经发表，但没有高质量的南海和代曲基因组，这阻碍了对大黄鱼精细遗传结构的评估，并对野生种群保护、渔业管理和种质资源开发产生不利影响。为了填补这一空白，我们使用PacBio和Hi-C技术对三个L. crocea种群进行了基因组测序。我们将每个基因组（~712 Mb）组装成24条染色体，序列N50为19.46 ~ 29.71 Mb，整合效率为88.13 ~ 92.80%。此外，还预测了26,851-28,133个蛋白质编码基因。三种地理种群的参考基因组为今后的遗传多样性保护与利用研究提供了重要的资源。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.