A high-quality genome assembly of the Spectacled Fulvetta (Fulvetta ruficapilla) endemic to China.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-11-20 DOI:10.1038/s41597-024-04094-2
Chen Yan, Si Si, Hong-Man Chen, Yu-Ting Zhang, Lu-Ming Liu, Fei Wu, Ming-Shan Wang
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Abstract

The Spectacled Fulvetta (Fulvetta ruficapilla) is the type species of Fulvetta, an evolutionarily distinct group whose species show a high degree of sympatry in distribution and phenotypic convergence. To pave the way for insights into their adaptive evolution and speciation, we have assembled the first high quality reference genome for F. ruficapilla using high-fidelity (HiFi) long-read and Hi-C sequencing technologies. The resulting assembly spans a total of ~1.21 Gb with a contig N50 of 18.8 Mb and scaffold N50 of 75.9 Mb, and has a BUSCO completeness of 97.0%. The quality assessment suggests a high standard in base accuracy, continuity, and completeness of the assembly, comparable or close to that of Vertebrate Genomes Project. On this basis, we have annotated 23,774 protein-coding genes, of which 18,832 are functionally identified. The availability of this high-quality genome provides a solid foundation for the future studies of evolution and local adaptation in birds.

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中国特有的斑纹福氏蝠(Fulvetta ruficapilla)的高质量基因组组装。
虹彩蝠(Fulvetta ruficapilla)是虹彩蝠的模式种,虹彩蝠是一个进化上截然不同的类群,其物种在分布和表型上表现出高度的同源性。为深入了解它们的适应性进化和物种分化,我们利用高保真(HiFi)长读数和 Hi-C 测序技术,首次组装了 F. ruficapilla 的高质量参考基因组。该基因组的总跨度约为 1.21 Gb,等位基因 N50 为 18.8 Mb,支架 N50 为 75.9 Mb,BUSCO 的完整性为 97.0%。质量评估结果表明,该汇编在碱基准确性、连续性和完整性方面都达到了很高的标准,可与脊椎动物基因组计划相媲美或接近。在此基础上,我们注释了 23,774 个编码蛋白质的基因,其中 18,832 个基因的功能已经确定。这一高质量基因组的问世为今后研究鸟类的进化和局部适应性奠定了坚实的基础。
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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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