The telomere-to-telomere gap-free reference genome and taxonomic reassessment of Siniperca roulei.

IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES
Min Jiang, Chenxi Zhao, Fengjiao Ma, Denghua Yin, Chenhe Wang, Jianbo Jian, Kai Liu
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引用次数: 0

Abstract

Siniperca roulei is primarily distributed in the eastern waters of China, with its population being both scarce and vulnerable. Research on this species remains limited, with few studies conducted on its biology and genetics, which hampers efforts to conserve its germplasm resources. To support breeding and conservation efforts, we generated a gap-free genome assembly using a combination of DNBSeq short reads, PacBio HiFi long reads, Nanopore ultra-long reads, and Hi-C data. The nearly telomere-to-telomere (T2T) genome of S. roulei spans 717.34 Mb, with a contig N50 of 30.25 Mb, and each chromosome is represented by a single contig. A total of 26,596 genes were predicted, with 87.97% functionally annotated. These high-precision genomic data provide valuable insights into the germplasm resources of S. roulei, offering crucial information for clarifying the taxonomic status and evolutionary history of sinipercids. These findings are significant for the conservation and sustainable use of its germplasm resources.

柔蛤端粒-端粒无间隙参考基因组及分类重新评估。
柔雷鳜主要分布在中国东部海域,种群稀少且脆弱。对该物种的研究仍然有限,对其生物学和遗传学的研究很少,这阻碍了对其种质资源的保护。为了支持育种和保护工作,我们使用DNBSeq短序列、PacBio HiFi长序列、Nanopore超长序列和Hi-C数据的组合生成了一个无间隙的基因组组装。红豆的近端粒到端粒(T2T)基因组全长717.34 Mb, N50为30.25 Mb,每条染色体由一个contig代表。共有26596个基因被预测,87.97%的基因被功能注释。这些高精确度的基因组数据为深入了解蛇麻属的种质资源提供了宝贵的信息,为阐明蛇麻属的分类地位和进化历史提供了重要的信息。这些发现对其种质资源的保护和可持续利用具有重要意义。
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来源期刊
GigaScience
GigaScience MULTIDISCIPLINARY SCIENCES-
CiteScore
15.50
自引率
1.10%
发文量
119
审稿时长
1 weeks
期刊介绍: GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.
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