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Deciphering the microbiota data from termite mound soil in South Africa using shotgun metagenomics

We present the metagenomic dataset of the microbial DNA of a termite mound in the North West Province of South Africa. This is the foremost account revealing the microbial diversity of a termite mound soil using the shotgun metagenomics approach in the Province. Next-generation sequencing of the com...

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Autores principales: Enagbonma, Ben Jesuorsemwen, Amoo, Adenike Eunice, Babalola, Olubukola Oluranti
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6889748/
https://www.ncbi.nlm.nih.gov/pubmed/31832528
http://dx.doi.org/10.1016/j.dib.2019.104802
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author Enagbonma, Ben Jesuorsemwen
Amoo, Adenike Eunice
Babalola, Olubukola Oluranti
author_facet Enagbonma, Ben Jesuorsemwen
Amoo, Adenike Eunice
Babalola, Olubukola Oluranti
author_sort Enagbonma, Ben Jesuorsemwen
collection PubMed
description We present the metagenomic dataset of the microbial DNA of a termite mound in the North West Province of South Africa. This is the foremost account revealing the microbial diversity of a termite mound soil using the shotgun metagenomics approach in the Province. Next-generation sequencing of the community DNA was carried out on an Illumina Miseq platform. The metagenome comprised of 7,270,818 sequences representing 1,172,099,467 bps with a mean length of 161 bps and 52% G + C content. The sequence data is accessible at the NCBI SRA under the bioproject number PRJNA526912. Metagenomic Rapid Annotations using Subsystems Technology (MG-RAST) was employed for community analysis and it was observed that 0.36% sequences were of archeal origin, 9.51% were eukaryotes and 90.01% were fit to bacteria. A total of 5 archeal, 27 bacterial, and 22 eukaryotic phyla were revealed. Abundant genera were Sphingomonas (6.00%), Streptomyces (5.00%), Sphingobium (4.00%), Sphingopyxis (3.00%), and Mycobacterium (3.00%), representing 19.23% in the metagenome. For functional examination, Cluster-of-Orthologous-Group (COG) based annotation showed that 46.44% sequences were metabolism associated and 17.45% grouped in the poorly characterized category. Subsystem based annotation method indicated that 14.00% sequences were carbohydrates, 13.00% were clustering-based subsystems, and 10.00% genes for amino acids and derivatives together with the presence of useful traits needed in the body of science.
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spelling pubmed-68897482019-12-12 Deciphering the microbiota data from termite mound soil in South Africa using shotgun metagenomics Enagbonma, Ben Jesuorsemwen Amoo, Adenike Eunice Babalola, Olubukola Oluranti Data Brief Genetics, Genomics and Molecular Biology We present the metagenomic dataset of the microbial DNA of a termite mound in the North West Province of South Africa. This is the foremost account revealing the microbial diversity of a termite mound soil using the shotgun metagenomics approach in the Province. Next-generation sequencing of the community DNA was carried out on an Illumina Miseq platform. The metagenome comprised of 7,270,818 sequences representing 1,172,099,467 bps with a mean length of 161 bps and 52% G + C content. The sequence data is accessible at the NCBI SRA under the bioproject number PRJNA526912. Metagenomic Rapid Annotations using Subsystems Technology (MG-RAST) was employed for community analysis and it was observed that 0.36% sequences were of archeal origin, 9.51% were eukaryotes and 90.01% were fit to bacteria. A total of 5 archeal, 27 bacterial, and 22 eukaryotic phyla were revealed. Abundant genera were Sphingomonas (6.00%), Streptomyces (5.00%), Sphingobium (4.00%), Sphingopyxis (3.00%), and Mycobacterium (3.00%), representing 19.23% in the metagenome. For functional examination, Cluster-of-Orthologous-Group (COG) based annotation showed that 46.44% sequences were metabolism associated and 17.45% grouped in the poorly characterized category. Subsystem based annotation method indicated that 14.00% sequences were carbohydrates, 13.00% were clustering-based subsystems, and 10.00% genes for amino acids and derivatives together with the presence of useful traits needed in the body of science. Elsevier 2019-11-13 /pmc/articles/PMC6889748/ /pubmed/31832528 http://dx.doi.org/10.1016/j.dib.2019.104802 Text en © 2019 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Genetics, Genomics and Molecular Biology
Enagbonma, Ben Jesuorsemwen
Amoo, Adenike Eunice
Babalola, Olubukola Oluranti
Deciphering the microbiota data from termite mound soil in South Africa using shotgun metagenomics
title Deciphering the microbiota data from termite mound soil in South Africa using shotgun metagenomics
title_full Deciphering the microbiota data from termite mound soil in South Africa using shotgun metagenomics
title_fullStr Deciphering the microbiota data from termite mound soil in South Africa using shotgun metagenomics
title_full_unstemmed Deciphering the microbiota data from termite mound soil in South Africa using shotgun metagenomics
title_short Deciphering the microbiota data from termite mound soil in South Africa using shotgun metagenomics
title_sort deciphering the microbiota data from termite mound soil in south africa using shotgun metagenomics
topic Genetics, Genomics and Molecular Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6889748/
https://www.ncbi.nlm.nih.gov/pubmed/31832528
http://dx.doi.org/10.1016/j.dib.2019.104802
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