韩国中温沼气反应器的代表性宏基因组。

IF 7.2 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-03 DOI:10.1038/s41597-024-04315-8
Darsha Prabhaharan, Young Wook Go, Hyunjin Kim, Seongcheol Kang, Byoung-In Sang
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引用次数: 0

摘要

通过有机废物的厌氧消化(AD)产生沼气在促进可持续性和关闭碳循环方面起着至关重要的作用。在过去的十年中,这推动了全球对产沼气微生物组的研究,使我们对AD植物内微生物多样性和代谢途径的理解取得了重大进展。然而,大量的知识差距仍然存在,特别是在了解韩国等国家参与沼气生产的特定微生物群落方面。目前的数据集通过提供有关韩国五个全规模中温生物气反应器的宏基因组的全面信息,解决了这些差距之一。从110 GB的原始DNA序列中,创建了401个宏基因组组装基因组(MAGs),其中包括42,301个注释基因。根据metagenome - assemble Genome (MIMAG)最小信息标准,187份mag(46.7%)为高质量。本文提供的数据有助于更广泛地了解沼气特定微生物群落,并为可持续沼气生产的未来研究和进步提供重要资源。
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Representative Metagenomes of Mesophilic Biogas Reactor Across South Korea.

Biogas production through the anaerobic digestion (AD) of organic waste plays a crucial role in promoting sustainability and closing the carbon cycle. Over the past decade, this has driven global research on biogas-producing microbiomes, leading to significant advances in our understanding of microbial diversity and metabolic pathways within AD plants. However, substantial knowledge gaps persist, particularly in understanding the specific microbial communities involved in biogas production in countries such as South Korea. The present dataset addresses one of these gaps by providing comprehensive information on the metagenomes of five full-scale mesophilic biogas reactors in South Korea. From 110 GB of raw DNA sequences, 401 metagenome-assembled genomes (MAGs) were created, which include 42,301 annotated genes. Of these, 187 MAGs (46.7%) were classified as high-quality based on Minimum Information about Metagenome-Assembled Genome (MIMAG) standards. The data presented here contribute to a broader understanding of biogas-specific microbial communities and offers a significant resource for future studies and advancements in sustainable biogas production.

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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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