A chromosome-level genome assembly and annotation of the Pseudorasbora elongata (Cypriniformes: Cyprinidae).

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Pan Wang, Denghua Yin, Min Jiang, Lingli Xie, Jie Liu, Yukuan Chen, Xinyue Wang, Yan Shao, Kai Liu
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引用次数: 0

Abstract

Pseudorasbora elongata is a unique small fish species endemic to China, distinguished by its striking body coloration resembling a "Chinese ink brush." Due to environmental changes and anthropogenic factors, its wild populations have declined, and it has been listed multiple times as an endangered species. However, the absence of a chromosomal-level reference for P. elongata has hindered our understanding of its population genetics and conservation biology. To address this gap, we present a chromosome-level genome assembly of P. elongata, generated using PacBio HiFi reads, Oxford Nanopore Technologies, and Hi-C data. We get a genome size of 1.4 Gb with a contig N50 of 34.4 Mb and a scafold N50 of 53.7 Mb. Telomeric sequences were identified at the ends of 42 telomeres across 25 chromosomes. Notably, we observed a high degree of collinearity between our assembly and the Pseudorasbora parva genome. This study provides valuable insights into the genetics, genomics, and evolutionary history of P. elongata, offering a foundation for future research and enabling the development of genetic conservation strategies.

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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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