A chromosome-level genome assembly of critically endangered Ochetobius elongatus.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2024-12-19 DOI:10.1038/s41597-024-04223-x

Lekang Li, Xuanguang Liang, Jiatong Zhang, Qun Xu, Li Wang, Xiaoping Gao, Xiangchun Song, Bao Zhang, Dan Huang, Hong Wang, Xianyong Wang, Zhen Luo, Chiping Kong, Jianguo Lu

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Abstract

Ochetobius elongatus, a critically endangered species found in the Yangtze River was the subject of our study in which we leveraged PacBio and Hi-C data to assemble a chromosome-scale genome. This assembly comprises 24 pseudo-chromosomes, yielding a genome size of 883.1 Mb with a scaffold N50 length of 35.1 Mb, indicative of a highly contiguous assembly. A BUSCO assessment ascertained the comprehensiveness of the genome at 98.3%. Annotation efforts identified 28,674 putative protein-coding genes, with 44.63% of the assembled genome annotated as repetitive sequences. Collinearity analysis between O. elongatus and two other species from the family Xenocyprididae revealed high collinearity, indicating good assembly quality of O. elongatus The completion of the O. elongatus genome assembly in this study signifies a critical advancement for its conservation, enabling deeper insights into its genetic diversity and facilitating the development of targeted preservation strategies.

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来源期刊

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.