Genomic profiling of Antarctic geothermal microbiomes using long-read, Hi-C, and single-cell techniques.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-09-19 DOI:10.1038/s41597-024-03875-z
Nu Ri Myeong, Yong-Hoe Choe, Seung Chul Shin, Jinhyun Kim, Woo Jun Sul, Mincheol Kim
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Abstract

Geothermal features in Antarctica provide favorable conditions for diverse microorganisms, yet their genomic diversity remains poorly understood. Here, we present an integrated dataset comprising PacBio HiFi and Hi-C metagenomic sequencing, along with single-cell amplified genomes (SAGs) from two high-altitude geothermal sites, Mount Melbourne and Mount Rittmann, in Antarctica. The long-read HiFi sequencing, coupled with Hi-C, enhances the understanding of microbiome diversity and functionality in this unique ecosystem by providing more complete and accurate genomic information. SAGs complement this by recovering rare microbial taxa and offering a strain-resolved perspective. This dataset aims to deepen our understanding of microbial evolution and ecology in Antarctic geothermal environments, and facilitate cross-comparison with other geothermal environments globally.

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利用长读数、Hi-C 和单细胞技术对南极地热微生物组进行基因组分析。
南极洲的地热特征为多种微生物的生长提供了有利条件,但人们对其基因组的多样性仍然知之甚少。在这里,我们展示了一个综合数据集,其中包括 PacBio HiFi 和 Hi-C 元基因组测序,以及来自南极洲墨尔本山和瑞特曼山这两个高海拔地热点的单细胞扩增基因组(SAGs)。长线程 HiFi 测序与 Hi-C 测序相结合,通过提供更完整、更准确的基因组信息,增强了对这一独特生态系统中微生物群多样性和功能的了解。SAG 则通过恢复稀有微生物类群和提供菌株分辨视角对其进行补充。该数据集旨在加深我们对南极地热环境中微生物进化和生态学的了解,并促进与全球其他地热环境的交叉比较。
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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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