Chromosome-level genome assembly of the ivory shell Babylonia areolata.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Yu Zou, Jingqiang Fu, Yuan Liang, Xuan Luo, Minghui Shen, Miaoqin Huang, Yexin Chen, Weiwei You, Caihuan Ke
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引用次数: 0

Abstract

The ivory shell Babylonia areolata is an economically important marine benthic gastropod known for its rapid growth and high nutritional value. B. areolata is distributed in Southeast Asia and the southeast coastal areas of China. In this study, we constructed a high-quality genome for B. areolata using PacBio, Illumina, and Hi-C sequencing technologies. The genome assembly comprised 35 chromosomal sequences with a total length of 1.65 Gb. The scaffold and contig N50 lengths were 53.17 Mb and 2.64 Mb, respectively, with repeat sequences constituting 64.46% of the genome. Furthermore, 26,130 protein-coding genes and 96.75% of the genome's BUSCOs were identified. This inaugural report of a B. areolata genome provides crucial foundational information for further investigations into the biology, genomics, and genetic improvement of economic traits of this species.

象牙贝壳 Babylonia areolata 的染色体级基因组组装。
象牙贝Babylonia areolata是一种具有重要经济价值的海洋底栖腹足类动物,以生长迅速和营养价值高而闻名。象牙贝分布于东南亚和中国东南沿海地区。在这项研究中,我们利用 PacBio、Illumina 和 Hi-C 测序技术构建了 B. areolata 的高质量基因组。基因组组装包括 35 个染色体序列,总长度为 1.65 Gb。支架和等位基因 N50 长度分别为 53.17 Mb 和 2.64 Mb,重复序列占基因组的 64.46%。此外,还鉴定了 26 130 个编码蛋白质的基因和基因组中 96.75% 的 BUSCOs。该首次报告的 B. areolata 基因组为进一步研究该物种的生物学、基因组学和经济性状的遗传改良提供了重要的基础信息。
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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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