来自 Kermadec 和 Diamantina Trenches 沉积物的元基因组测序和 982 个微生物基因组。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-10-01 DOI:10.1038/s41597-024-03902-z
Yingdong Li, Hao Liu, Yao Xiao, Hongmei Jing
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引用次数: 0

摘要

深海海沟是探索微生物群落在高度特化的深海环境中的生存和进化策略的一个有趣的生态系统。本文从 Kermadec 和 Diamantina 海沟采集的沉积物样本中获得了 29 个元基因组。值得注意的是,这些样本覆盖了不同的采样深度(从 5321 米到 9415 米)和沉积物本身的不同层(Kermadec 海沟为 0~40 厘米,Diamantina 海沟为 0~24 厘米)。通过元基因组分选过程,我们重建了 982 个完整度大于 60%、污染度大于 90%的元基因组(MAGs),另有 331 个基因组的完整度大于 80%。对 MAGs 的系统发生组分析表明,几乎所有的 MAGs 都与已知的栽培分离菌关系密切。大量的细菌 MAG 隶属于蛋白菌门、平面菌门、硝化菌门、酸性菌门、放线菌门和绿藻门,而大量的古细菌门则隶属于纳米古细菌门和热蛋白菌门。这些结果为进一步研究海沟中深海微生物的多样性、分布和生态功能提供了数据集。
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Metagenome sequencing and 982 microbial genomes from Kermadec and Diamantina Trenches sediments.

Deep-sea trenches representing an intriguing ecosystem for exploring the survival and evolutionary strategies of microbial communities in the highly specialized deep-sea environments. Here, 29 metagenomes were obtained from sediment samples collected from Kermadec and Diamantina trenches. Notably, those samples covered a varying sampling depths (from 5321 m to 9415 m) and distinct layers within the sediment itself (from 0~40 cm in Kermadec trench and 0~24 cm in Diamantina trench). Through metagenomic binning process, we reconstructed 982 metagenome assembled genomes (MAGs) with completeness >60% and contamination <5%. Within them, completeness of 351 MAGs were >90%, while an additional 331 were >80%. Phylogenomic analysis for the MAGs revealed nearly all of them were distantly related to known cultivated isolates. The abundant bacterial MAGs affiliated to phyla of Proteobacteria, Planctomycetota, Nitrospirota, Acidobacteriota, Actinobacteriota, and Chlorofexota, while the abundant archaeal phyla affiliated with Nanoarchaeota and Thermoproteota. These results provide a dataset available for further interrogation of diversity, distribution and ecological function of deep-sea microbes existed in the trenches.

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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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