利用 PacBio HiFi 和 Hi-C 测序技术进行鲮鱼(Cirrhinus molitorella)染色体级基因组组装。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Haiyang Liu, Tongxin Cui, Huijuan Liu, Jin Zhang, Qing Luo, Shuzhan Fei, Kunci Chen, Xinping Zhu, Chunkun Zhu, Bingjie Li, Lingzhao Fang, Jian Zhao, Mi Ou
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引用次数: 0

摘要

鲮鱼(Cirrhinus molitorella)是一种重要的经济养殖鱼类,由于其适应性强、产量高,主要分布在华南和东南亚地区。尽管鲮鱼具有重要的经济价值,但其基因组信息的匮乏制约了详细的遗传研究和育种工作。在这项研究中,我们利用 PacBio HiFi 长线程测序技术和 Hi-C 技术,生成了鲮鱼染色体组水平的基因组。该基因组横跨 1,033.41 Mb,99.82% 的基因分布在 25 条染色体上,令人印象深刻。等位基因 N50 和支架 N50 分别为 33.29 Mb 和 39.86 Mb。鲮鱼基因组组装的 BUSCO 得分为 98.05%,凸显了鲮鱼基因组组装的完整性。我们预测了 25,865 个蛋白质编码基因,BUSCO 得分为 96.54%,其中 91.83% 的基因有功能注释。约 52.21% 的基因组由重复元件组成。这一高保真基因组组装是推动鲮鱼及相关物种分子育种、比较基因组学和进化研究的重要资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Chromosome-level genome assembly of the mud carp (Cirrhinus molitorella) using PacBio HiFi and Hi-C sequencing.

The mud carp (Cirrhinus molitorella) is an important economic farmed fish, mainly distributed in South China and Southeast Asia due to its strong adaptability and high yield. Despite its economic importance, the paucity of genomic information has constrained detailed genetic research and breeding efforts. In this study, we utilized PacBio HiFi long-read sequencing and Hi-C technologies to generate a meticulously assembled chromosome-level genome of the mud carp. This assembly spans 1,033.41 Mb, with an impressive 99.82% distributed across 25 chromosomes. The contig N50 and scaffold N50 are 33.29 Mb and 39.86 Mb, respectively. The completeness of the mud carp genome assembly is highlighted by a BUSCO score of 98.05%. We predict 25,865 protein-coding genes, with a BUSCO score of 96.54%, and functional annotations for 91.83% of these genes. Approximately 52.21% of the genome consists of repeat elements. This high-fidelity genome assembly is a vital resource for advancing molecular breeding, comparative genomics, and evolutionary studies of the mud carp and related species.

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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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