自交四倍体 Misgurnus anguillicaudatus 的染色体尺度和单倍型解析基因组组装。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Bing Sun, Qingshan Li, Yihui Mei, Yunbang Zhang, Yuxuan Zheng, Yuwei Huang, Xinxin Xiao, Jianwei Zhang, Gao Jian, Xiaojuan Cao
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引用次数: 0

摘要

在自然界中,二倍体和四倍体是多倍体进化的两种常见类型。泥鳅(Misgurnus anguillicaudatus)是一种既有二倍体又有四倍体的特殊鱼类。然而,重建其自体四倍体基因组的四个单倍型仍是一个悬而未决的问题。在这里,我们首次生成了单倍型解析的自交四倍体鳗鲡染色体级基因组,其大小为 4.76 Gb,等位基因 N50 为 6.78 Mb,支架 N50 为 44.11 Mb。我们发现了约 2.9 Gb(占基因组的 61.03%)的重复序列,并预测了 91,485 个编码蛋白质的基因。此外,等位基因表达水平表明,自交四倍体泥鳅基因组中不存在明显的显性单倍型。该基因组将为揭示多倍体形成和进化机制、适应环境变化提供宝贵的生物模型,并有利于水产养殖应用和生物多样性保护。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Chromosome-scale and haplotype-resolved genome assembly of the autotetraploid Misgurnus anguillicaudatus.

In nature, diploids and tetraploids are two common types of polyploid evolution. Misgurnus anguillicaudatus (mud loach) is a remarkable fish species that exhibits both diploid and tetraploid forms. However, reconstructing the four haplotypes of its autotetraploid genome remains unresolved. Here, we generated the first haplotype-resolved, chromosome-level genome of autotetraploid M. anguillicaudatus with a size of 4.76 Gb, contig N50 of 6.78 Mb, and scaffold N50 of 44.11 Mb. We identified approximately 2.9 Gb (61.03% of genome) of repetitive sequences and predicted 91,485 protein-coding genes. Moreover, allelic gene expression levels indicated the absence of significant dominant haplotypes within the autotetraploid loach genome. This genome will provide a valuable biological model for unraveling the mechanisms of polyploid formation and evolution, adaptation to environmental changes, and benefit for aquaculture applications and biodiversity conservation.

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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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