Chromosome-scale assembly of the Xenocypris davidi using PacBio HiFi reads and Hi-C technologies.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-03-18 DOI:10.1038/s41597-025-04800-8
Tiezhu Yang, Liangjie Zhao, Chaoqun Su, Xusheng Guo, Xinliang Peng, Shijie Yang, Gaoyou Yao
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Abstract

Xenocypris davidi is a benthic fish species widely distributed in the water systems south of the Yellow River in China, playing a significant role in aquatic ecosystems. Despite its ecological and economic importance, genomic resources for X. davidi are limited, hindering a comprehensive understanding of its evolutionary adaptations and genetic improvements. This study presents the first chromosome-level genome assembly of X. davidi, utilizing PacBio long-reads, Illumina short reads, and Hi-C sequencing data. The genome assembly spans 1.05 Gb with a scaffold N50 length of 33.99 Mb, and 95.12% of the genome sequence was successfully anchored onto 24 pseudochromosomes. We identified 27,360 protein-coding genes, of which 26,672 were functionally annotated. This genome sequence provides a valuable resource for exploring the molecular basis of agronomic traits in X. davidi and will facilitate its genetic enhancement.

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大菱鲆(Xenocypris davidi)是一种底栖鱼类,广泛分布于中国黄河以南水系,在水生生态系统中发挥着重要作用。尽管其在生态和经济方面具有重要意义,但大菱鲆的基因组资源有限,阻碍了对其进化适应和遗传改良的全面了解。本研究利用 PacBio 长读数、Illumina 短读数和 Hi-C 测序数据,首次完成了 X. davidi 的染色体级基因组组装。基因组组装的跨度为 1.05 Gb,支架 N50 长度为 33.99 Mb,95.12% 的基因组序列被成功锚定到 24 个假染色体上。我们发现了 27,360 个编码蛋白质的基因,其中 26,672 个已进行了功能注释。该基因组序列为探索 X. davidi 农艺性状的分子基础提供了宝贵的资源,并将促进其遗传改良。
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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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