Chromosome-level genome assembly and annotation of the skinnycheek lanternfish Benthosema ptertum.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2024-10-30 DOI:10.1038/s41597-024-04039-9

Qiaohong Liu, Xiaoying Cao, Lisheng Wu, Huan Wang, Hai Li, Longshan Lin, Shufang Liu, Shaoxiong Ding

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Abstract

Lanternfish not only boast the most abundant biomass among marine fish species but also play a vital role in marine ecosystems. As one of the lanternfish species with the highest global catch, the skinnycheek lanternfish (Benthosema pterotum) is widely distributed in the Indo-Pacific region, playing a pivotal role in the marine biological pump. This study constructed the first chromosome-level genome of B. pterotum using a combination of short-read sequencing, PacBio, and Hi-C sequencing technologies. The genome size of B. pterotum is 1,272.53 Mb, with a contig N50 of 810 Kb and a scaffold N50 of 54.49 M. More than 99.65% of contigs were successfully anchored onto 24 pseudochromosomes, and 95.7% of BUSCO genes were identified within the genome, demonstrating the high level of completeness in genome assembly. A total of 24,934 protein-coding genes were predicted, of which 99.02% were functionally annotated. The successful assembly of a high-quality genome for B. pterotum provides valuable genetic resources for better understanding its biological characteristics and potentially those of all lanternfish species.

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瘦颊灯笼鱼 Benthosema ptertum 染色体级基因组组装与注释。

灯笼鱼不仅拥有海洋鱼类中最丰富的生物量，而且在海洋生态系统中发挥着至关重要的作用。作为全球捕获量最高的灯笼鱼物种之一，裸颊灯笼鱼（Benthosema pterotum）广泛分布于印度洋-太平洋地区，在海洋生物泵中发挥着举足轻重的作用。本研究结合短线程测序、PacBio 和 Hi-C 测序技术，首次构建了 B. pterotum 的染色体级基因组。B.pterotum的基因组大小为1,272.53 Mb，等位基因N50为810 Kb，支架N50为54.49 M。超过 99.65% 的等位基因被成功锚定在 24 个假染色体上，95.7% 的 BUSCO 基因在基因组内被鉴定，这表明基因组组装的完整性达到了很高的水平。共预测了 24,934 个编码蛋白质的基因，其中 99.02% 的基因已进行了功能注释。成功地组装出高质量的B. pterotum基因组为更好地了解其生物学特征以及所有灯笼鱼物种的生物学特征提供了宝贵的遗传资源。

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.