Dataset Information

Semi-subterranean environment and soil metagenomic datasets of the Gyeongju Seokbinggo (stone ice storage) in South Korea.

ABSTRACT: The Seokbinggo is an ice cellar made of stone to store ice in the 1700s. The Seokbinggo, a traditional Korean stone architecture, can keep ice collected in winter until summer because the semi-subterranean structure utilizes the natural environment, and the insulation design is effective. However, these structures and scientific designs are not used as ice storage and are easily damaged by biological contamination. We present the environmental data of the inside and the metagenomic dataset of the soil samples from the Seokbinggo. Next-generation sequencing was carried out on an Illumina MiSeq platform. The raw sequence data used for analysis is available in NCBI under the Sequence Read Archive (SRA) with BioProject No. PRJNA727939 and SRA accession Nos. SRX10976613, SRX10976614, SRX10976615, SRX10976616, SRX10976617, SRX10976618, SRX10976619, SRX10976620. Environmental data, including data from Korea Meteorological Administration, is available in the Mendeley data repository with DOI: 10.17632/2r8gpg7pxn.1.

SUBMITTER: Kim Y

PROVIDER: S-EPMC9157459 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Semi-subterranean environment and soil metagenomic datasets of the Gyeongju Seokbinggo (stone ice storage) in South Korea.

Kim YoungHee Y Lim Boa B Park JiHee J Kim SooJi S

Data in brief 20220521

The Seokbinggo is an ice cellar made of stone to store ice in the 1700s. The Seokbinggo, a traditional Korean stone architecture, can keep ice collected in winter until summer because the semi-subterranean structure utilizes the natural environment, and the insulation design is effective. However, these structures and scientific designs are not used as ice storage and are easily damaged by biological contamination. We present the environmental data of the inside and the metagenomic dataset of th ...[more]

PMID: 35664653

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Project description:BackgroundShotgun metagenomics based on untargeted sequencing can explore the taxonomic profile and the function of unknown microorganisms in samples, and complement the shortage of amplicon sequencing. Binning assembled sequences into individual groups, which represent microbial genomes, is the key step and a major challenge in metagenomic research. Both supervised and unsupervised machine learning methods have been employed in binning. Genome binning belonging to unsupervised method clusters contigs into individual genome bins by machine learning methods without the assistance of any reference databases. So far a lot of genome binning tools have emerged. Evaluating these genome tools is of great significance to microbiological research. In this study, we evaluate 15 genome binning tools containing 12 original binning tools and 3 refining binning tools by comparing the performance of these tools on chicken gut metagenomic datasets and the first CAMI challenge datasets.ResultsFor chicken gut metagenomic datasets, original genome binner MetaBat, Groopm2 and Autometa performed better than other original binner, and MetaWrap combined the binning results of them generated the most high-quality genome bins. For CAMI datasets, Groopm2 achieved the highest purity (> 0.9) with good completeness (> 0.8), and reconstructed the most high-quality genome bins among original genome binners. Compared with Groopm2, MetaBat2 had similar performance with higher completeness and lower purity. Genome refining binners DASTool predicated the most high-quality genome bins among all genomes binners. Most genome binner performed well for unique strains. Nonetheless, reconstructing common strains still is a substantial challenge for all genome binner.ConclusionsIn conclusion, we tested a set of currently available, state-of-the-art metagenomics hybrid binning tools and provided a guide for selecting tools for metagenomic binning by comparing range of purity, completeness, adjusted rand index, and the number of high-quality reconstructed bins. Furthermore, available information for future binning strategy were concluded.

Dataset Information

Semi-subterranean environment and soil metagenomic datasets of the Gyeongju Seokbinggo (stone ice storage) in South Korea.

Publications

Semi-subterranean environment and soil metagenomic datasets of the Gyeongju Seokbinggo (stone ice storage) in South Korea.

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