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From microbial community structure to metabolic inference using paprica
Microbial taxonomic marker gene studies using 16S rRNA gene amplicon sequencing provide an understanding of microbial community structure and diversity; however, it can be difficult to infer the functionality of microbes in the ecosystem from these data. Here, we show how to predict metabolism from...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8672035/ https://www.ncbi.nlm.nih.gov/pubmed/34950886 http://dx.doi.org/10.1016/j.xpro.2021.101005 |
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author | Erazo, Natalia G. Dutta, Avishek Bowman, Jeff S. |
author_facet | Erazo, Natalia G. Dutta, Avishek Bowman, Jeff S. |
author_sort | Erazo, Natalia G. |
collection | PubMed |
description | Microbial taxonomic marker gene studies using 16S rRNA gene amplicon sequencing provide an understanding of microbial community structure and diversity; however, it can be difficult to infer the functionality of microbes in the ecosystem from these data. Here, we show how to predict metabolism from phylogeny using the paprica pipeline. This approach allows resolution at the strain and species level for select regions on the prokaryotic phylogenetic tree and provides an estimate of gene and metabolic pathway abundance. For complete details on the use and execution of this protocol, please refer to Erazo and Bowman (2021). |
format | Online Article Text |
id | pubmed-8672035 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-86720352021-12-22 From microbial community structure to metabolic inference using paprica Erazo, Natalia G. Dutta, Avishek Bowman, Jeff S. STAR Protoc Protocol Microbial taxonomic marker gene studies using 16S rRNA gene amplicon sequencing provide an understanding of microbial community structure and diversity; however, it can be difficult to infer the functionality of microbes in the ecosystem from these data. Here, we show how to predict metabolism from phylogeny using the paprica pipeline. This approach allows resolution at the strain and species level for select regions on the prokaryotic phylogenetic tree and provides an estimate of gene and metabolic pathway abundance. For complete details on the use and execution of this protocol, please refer to Erazo and Bowman (2021). Elsevier 2021-12-11 /pmc/articles/PMC8672035/ /pubmed/34950886 http://dx.doi.org/10.1016/j.xpro.2021.101005 Text en © 2021 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Protocol Erazo, Natalia G. Dutta, Avishek Bowman, Jeff S. From microbial community structure to metabolic inference using paprica |
title | From microbial community structure to metabolic inference using paprica |
title_full | From microbial community structure to metabolic inference using paprica |
title_fullStr | From microbial community structure to metabolic inference using paprica |
title_full_unstemmed | From microbial community structure to metabolic inference using paprica |
title_short | From microbial community structure to metabolic inference using paprica |
title_sort | from microbial community structure to metabolic inference using paprica |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8672035/ https://www.ncbi.nlm.nih.gov/pubmed/34950886 http://dx.doi.org/10.1016/j.xpro.2021.101005 |
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