Chromosome-level genome assembly of Indo-Pacific king mackerel (Scomberomorus guttatus).

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Yan Gao, Sailan Liu, Qilin Gutang, Chengzhang Li, Xiaoqi Lin, Bo Liang, Ping Li, Jianqing Lin, Wenhua Liu
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引用次数: 0

Abstract

The Indo-Pacific king mackerel, scientifically known as Scomberomorus guttatus, is a valued marine species that holds significant commercial importance in the Indo-Pacific region. However, the lack of genomic resources has hindered a comprehensive understanding of this species. In this study, we constructed a genome of Indo-Pacific king mackerel at the chromosome level using a combination of PacBio HiFi reads and a chromosome contact map (Hi-C). The resulting genome had high contig and scaffold N50 values of 8.84 Mb and 32.9 Mb, respectively. In the genome assembly, which is 797.66 Mb in size and consists of 24 chromosomes, we also identified 35.89% repetitive elements and predicted 25,886 protein-coding genes. Our study not only benefits to reveal the possible mechanism of adaptive evolution in this fish, but also offers insight for the future sustainable management of these valuable biological resources.

印度-太平洋马鲛鱼(Scomberomorus guttatus)染色体级基因组组装。
印度-太平洋鲭王,学名为Scomberomorus guttatus,是一种珍贵的海洋物种,在印度-太平洋地区具有重要的商业价值。然而,基因组资源的缺乏阻碍了对该物种的全面了解。在这项研究中,我们结合 PacBio HiFi 读数和染色体接触图谱(Hi-C),在染色体水平上构建了印度洋-太平洋鲭王的基因组。所得到的基因组具有较高的等位基因和支架 N50 值,分别为 8.84 Mb 和 32.9 Mb。在由 24 条染色体组成、大小为 797.66 Mb 的基因组组装中,我们还发现了 35.89% 的重复元件,并预测了 25,886 个蛋白编码基因。我们的研究不仅有助于揭示这种鱼类适应性进化的可能机制,还为这些宝贵生物资源未来的可持续管理提供了启示。
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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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