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Comparing microbial community compositions of biogas and sewage treatment plants by analyzing 16S rRNA gene data
This article contains data of bacterial and archaeal community compositions and process parameters of 16 biogas (BPs) and ten sewage treatment plants (STPs). Nucleic acids of BPs were extracted using a CTAB-based method while a phenol-chloroform-based method was applied for STPs. Amplicon sequencing...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6197571/ https://www.ncbi.nlm.nih.gov/pubmed/30364597 http://dx.doi.org/10.1016/j.dib.2018.09.118 |
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author | Buettner, Christian Noll, Matthias |
author_facet | Buettner, Christian Noll, Matthias |
author_sort | Buettner, Christian |
collection | PubMed |
description | This article contains data of bacterial and archaeal community compositions and process parameters of 16 biogas (BPs) and ten sewage treatment plants (STPs). Nucleic acids of BPs were extracted using a CTAB-based method while a phenol-chloroform-based method was applied for STPs. Amplicon sequencing data of the 16S rRNA gene was achieved by MiSeq-technology. Raw data were processed and statistically analyzed by several approaches including network analyses by using molecular ecological network analysis (MENA). Nodes of each network (BPs and STPs) were classified as generalists and peripherals to identify key players for a stable biogas production. Network parameters as well as interaction of generalistic species were compared between both plant types. |
format | Online Article Text |
id | pubmed-6197571 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-61975712018-10-24 Comparing microbial community compositions of biogas and sewage treatment plants by analyzing 16S rRNA gene data Buettner, Christian Noll, Matthias Data Brief Economics, Econometrics and Finance This article contains data of bacterial and archaeal community compositions and process parameters of 16 biogas (BPs) and ten sewage treatment plants (STPs). Nucleic acids of BPs were extracted using a CTAB-based method while a phenol-chloroform-based method was applied for STPs. Amplicon sequencing data of the 16S rRNA gene was achieved by MiSeq-technology. Raw data were processed and statistically analyzed by several approaches including network analyses by using molecular ecological network analysis (MENA). Nodes of each network (BPs and STPs) were classified as generalists and peripherals to identify key players for a stable biogas production. Network parameters as well as interaction of generalistic species were compared between both plant types. Elsevier 2018-10-04 /pmc/articles/PMC6197571/ /pubmed/30364597 http://dx.doi.org/10.1016/j.dib.2018.09.118 Text en © 2018 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 | Economics, Econometrics and Finance Buettner, Christian Noll, Matthias Comparing microbial community compositions of biogas and sewage treatment plants by analyzing 16S rRNA gene data |
title | Comparing microbial community compositions of biogas and sewage treatment plants by analyzing 16S rRNA gene data |
title_full | Comparing microbial community compositions of biogas and sewage treatment plants by analyzing 16S rRNA gene data |
title_fullStr | Comparing microbial community compositions of biogas and sewage treatment plants by analyzing 16S rRNA gene data |
title_full_unstemmed | Comparing microbial community compositions of biogas and sewage treatment plants by analyzing 16S rRNA gene data |
title_short | Comparing microbial community compositions of biogas and sewage treatment plants by analyzing 16S rRNA gene data |
title_sort | comparing microbial community compositions of biogas and sewage treatment plants by analyzing 16s rrna gene data |
topic | Economics, Econometrics and Finance |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6197571/ https://www.ncbi.nlm.nih.gov/pubmed/30364597 http://dx.doi.org/10.1016/j.dib.2018.09.118 |
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