Chromosome-level genome assembly and annotation of a sea toad (Chaunax sp.).

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Zifeng Zhan, Yanshuo Liang, Jiehong Wei, Jing Liu, Kuidong Xu
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引用次数: 0

Abstract

The sea toad genus Chaunax is a group of small benthic fishes that predominantly inhabiting the deep seas of the Atlantic, Indian, and Pacific Oceans. Although they have the potential to make excellent systems for studies of evolutionary adaptation to deep-sea environments, genomic research on Chaunax has been hindered by a scarcity of high-quality genomic resources. We present a chromosome-scale genome assembly of a Chaunax specimen generated using PacBio long-read sequencing and high-throughput chromosome conformation capture technology. The size of the assembled genome was 706.94 Mb, with a contig N50 of 15.24 Mb and scaffold N50 of 29.42 Mb. Approximately 96.11% of assembled sequences were anchored and oriented onto 24 pseudo-chromosomes. The genome contained 213.47 Mb repetitive sequences, 25,280 protein-coding genes, and 5,090 non-coding RNAs. The high ratio of complete BUSCO genes (97.20%) indicates high quality of genome assembly. The chromosomal-level reference genome of Chaunax sp. provides a preliminary molecular basis for understanding deep-sea adaptation and phenotypic evolution as well as an important reference for whole-genome sequencing of related species.

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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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