早期分化的浮游硅藻 Paralia guyana 的高质量基因组。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-10-30 DOI:10.1038/s41597-024-03843-7
Jianbo Jian, Feichao Du, Binhu Wang, Xiaodong Fang, Thomas Ostenfeld Larsen, Yuhang Li, Eva C Sonnenschein
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引用次数: 0

摘要

硅藻 Paralia guyana 是一种浮游微藻,是早期分化硅藻的代表之一。P. guyana 既能在浮游生物栖息地也能在底栖生物栖息地生长,对赤潮事件的发生做出了重要贡献。虽然已经对十几个硅藻基因组进行了测序,但早期分化硅藻的身份仍然难以确定。由于缺乏高质量的基因组组装,人们对 P. guyana 的进化支系和生态适应机制的了解受到了限制。在本研究中,利用 PacBio 单分子测序技术首次建立了早期分化硅藻 P. guyana 的高质量基因组。组装的基因组大小为 558.85 Mb,是有记录以来最大的硅藻基因组,等位基因 N50 大小为 26.06 Mb。在 P. guyana 基因组中,共预测出 27 121 个编码蛋白质的基因,其中 22 904 个预测基因(84.45%)已进行了功能注释。这些数据和分析为浮游微藻物种提供了创新的基因组资源,并揭示了硅藻的进化起源。
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A high-quality genome of the early diverging tychoplanktonic diatom Paralia guyana.

The diatom Paralia guyana is a tychoplanktonic microalgal species that represents one of the early diverging diatoms. P. guyana can thrive in both planktonic and benthic habitats, making a significant contribution to the occurrence of red tide events. Although a dozen diatom genomes have been sequenced, the identity of the early diverging diatoms remains elusive. The understanding of the evolutionary clades and mechanisms of ecological adaptation in P. guyana is limited by the absence of a high-quality genome assembly. In this study, the first high-quality genome assembly for the early diverging diatom P. guyana was established using PacBio single molecular sequencing. The assembled genome has a size of 558.85 Mb, making it the largest diatom genome on record, with a contig N50 size of 26.06 Mb. A total of 27,121 protein-coding genes were predicted in the P. guyana genome, of which 22,904 predicted genes (84.45%) were functionally annotated. This data and analysis provide innovative genomic resources for tychoplanktonic microalgal species and shed light on the evolutionary origins of diatoms.

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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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