番茄后腿鱼(Cephalopholis sonnerati)的无间隙端粒-端粒单倍型组装。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-11-22 DOI:10.1038/s41597-024-04093-3
Sheng Lu, Yang Liu, Ming Li, Qijin Ge, Chongwei Wang, Yu Song, Bo Zhou, Songlin Chen
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引用次数: 0

摘要

番茄后腿鱼(Cephalopholis sonnerati)是近年来新出现的一种具有重要经济价值的石斑鱼。随着测序技术和组装方法的日益成熟,更高质量的参考基因组变得既容易获得又有必要。在本研究中,我们展示了番茄后腿鱼的两个端粒到端粒(T2T)无间隙单倍型组装,长度分别为 1039.53 Mb(YSFRI_Csonn_HA_1.0,N50 43.83 Mb)和 1039.91 Mb(YSFRI_Csonn_HB_1.0,N50 44.09 Mb)。来自下一代测序、ONT 超长测序和 PacBio HiFi 测序的读数与这两个集合对齐时,映射率超过 99.8%。使用 Merqury 进行的评估表明,这两个集合的准确性很高,平均质量值分别为 51.80 和 51.83。完整的 BUSCOs 百分比分别达到 97.9% 和 97.8%,每个集合中分别推断出 23,270 和 23,184 个蛋白质编码基因。此外,还成功进行了端粒鉴定、中心粒预测和重复序列注释。这两个组合为 C. sonnerati 的遗传分析和分子遗传育种技术的发展提供了坚实的基础。
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Gap-free telomere-to-telomere haplotype assembly of the tomato hind (Cephalopholis sonnerati).

The tomato hind (Cephalopholis sonnerati) is an emerging economically important grouper in recent years. With the increasing maturity of sequencing technologies and assembly methodologies, a higher quality reference genome has become both accessible and necessary. In this study, we present two telomere-to-telomere (T2T) gap-free haplotype assemblies of the tomato hind with lengths of 1039.53 Mb (YSFRI_Csonn_HA_1.0, N50 43.83 Mb) and 1039.91 Mb (YSFRI_Csonn_HB_1.0, N50 44.09 Mb). Reads from next-generation sequencing, ONT ultra-long sequencing, and PacBio HiFi sequencing exhibited mapping rates exceeding 99.8% when aligned to these two assemblies. Evaluation using Merqury indicated high accuracy for both assemblies, with average quality values of 51.80 and 51.83, respectively. Percentages of 97.9% and 97.8% of complete BUSCOs were achieved, and a total of 23,270 and 23,184 protein-code genes were inferred in each assembly. Moreover, telomere identification, centromere prediction, and repetitive sequence annotation were also successfully performed. These two assemblies provide robust foundation for the genetic analysis and development of molecular genetic breeding technologies in C. sonnerati.

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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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