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Achieving pan-microbiome biological insights via the dbBact knowledge base
16S rRNA amplicon sequencing provides a relatively inexpensive culture-independent method for studying microbial communities. Although thousands of such studies have examined diverse habitats, it is difficult for researchers to use this vast trove of experiments when interpreting their own findings...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10359611/ https://www.ncbi.nlm.nih.gov/pubmed/37326027 http://dx.doi.org/10.1093/nar/gkad527 |
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author | Amir, Amnon Ozel, Eitan Haberman, Yael Shental, Noam |
author_facet | Amir, Amnon Ozel, Eitan Haberman, Yael Shental, Noam |
author_sort | Amir, Amnon |
collection | PubMed |
description | 16S rRNA amplicon sequencing provides a relatively inexpensive culture-independent method for studying microbial communities. Although thousands of such studies have examined diverse habitats, it is difficult for researchers to use this vast trove of experiments when interpreting their own findings in a broader context. To bridge this gap, we introduce dbBact – a novel pan-microbiome resource. dbBact combines manually curated information from studies across diverse habitats, creating a collaborative central repository of 16S rRNA amplicon sequence variants (ASVs), which are assigned multiple ontology-based terms. To date dbBact contains information from more than 1000 studies, which include 1500000 associations between 360000 ASVs and 6500 ontology terms. Importantly, dbBact offers a set of computational tools allowing users to easily query their own datasets against the database. To demonstrate how dbBact augments standard microbiome analysis we selected 16 published papers, and reanalyzed their data via dbBact. We uncovered novel inter-host similarities, potential intra-host sources of bacteria, commonalities across different diseases and lower host-specificity in disease-associated bacteria. We also demonstrate the ability to detect environmental sources, reagent-borne contaminants, and identify potential cross-sample contaminations. These analyses demonstrate how combining information across multiple studies and over diverse habitats leads to better understanding of underlying biological processes. |
format | Online Article Text |
id | pubmed-10359611 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-103596112023-07-22 Achieving pan-microbiome biological insights via the dbBact knowledge base Amir, Amnon Ozel, Eitan Haberman, Yael Shental, Noam Nucleic Acids Res Data Resources and Analyses 16S rRNA amplicon sequencing provides a relatively inexpensive culture-independent method for studying microbial communities. Although thousands of such studies have examined diverse habitats, it is difficult for researchers to use this vast trove of experiments when interpreting their own findings in a broader context. To bridge this gap, we introduce dbBact – a novel pan-microbiome resource. dbBact combines manually curated information from studies across diverse habitats, creating a collaborative central repository of 16S rRNA amplicon sequence variants (ASVs), which are assigned multiple ontology-based terms. To date dbBact contains information from more than 1000 studies, which include 1500000 associations between 360000 ASVs and 6500 ontology terms. Importantly, dbBact offers a set of computational tools allowing users to easily query their own datasets against the database. To demonstrate how dbBact augments standard microbiome analysis we selected 16 published papers, and reanalyzed their data via dbBact. We uncovered novel inter-host similarities, potential intra-host sources of bacteria, commonalities across different diseases and lower host-specificity in disease-associated bacteria. We also demonstrate the ability to detect environmental sources, reagent-borne contaminants, and identify potential cross-sample contaminations. These analyses demonstrate how combining information across multiple studies and over diverse habitats leads to better understanding of underlying biological processes. Oxford University Press 2023-06-16 /pmc/articles/PMC10359611/ /pubmed/37326027 http://dx.doi.org/10.1093/nar/gkad527 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Data Resources and Analyses Amir, Amnon Ozel, Eitan Haberman, Yael Shental, Noam Achieving pan-microbiome biological insights via the dbBact knowledge base |
title | Achieving pan-microbiome biological insights via the dbBact knowledge base |
title_full | Achieving pan-microbiome biological insights via the dbBact knowledge base |
title_fullStr | Achieving pan-microbiome biological insights via the dbBact knowledge base |
title_full_unstemmed | Achieving pan-microbiome biological insights via the dbBact knowledge base |
title_short | Achieving pan-microbiome biological insights via the dbBact knowledge base |
title_sort | achieving pan-microbiome biological insights via the dbbact knowledge base |
topic | Data Resources and Analyses |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10359611/ https://www.ncbi.nlm.nih.gov/pubmed/37326027 http://dx.doi.org/10.1093/nar/gkad527 |
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