Microbial profiling of the East Siberian Sea sediments using 16S rRNA gene and metagenome sequencing.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Jehyun Jeon, Yerin Park, Dong-Hun Lee, Ji-Hoon Kim, Young Keun Jin, Jong Kuk Hong, Yung Mi Lee
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引用次数: 0

Abstract

The Arctic Ocean is experiencing significant global warming, leading to reduced sea-ice cover, submarine permafrost thawing, and increased river discharge. The East Siberian Sea (ESS) undergoes more significant terrestrial inflow from coastal erosion and river runoff than other Arctic seas. Despite extensive research on environmental changes, microbial communities and their functions in the ESS, which are closely related to environmental conditions, remain largely unexplored. Here, we investigated microbial communities in ESS surface sediments spanning latitudes from 73°N to 77°N using 16S rRNA amplicon sequencing, and reconstructed 211 metagenome-assembled genomes (MAGs) using shotgun metagenome sequencing. Taxonomic analysis identified 209 bacterial MAGs, with the predominant phyla Pseudomonadota (n = 82), Actinobacteriota (n = 38), Desulfobacterota (n = 23), along with 2 archaeal MAGs of Thermoproteota. Notably, 86% of the MAGs (n = 183) could not be classified into known species, indicating the potential presence of novel and unidentified microorganisms in the ESS. This dataset provides invaluable information on the microbial diversity and ecological functions in the rapidly changing ESS.

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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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