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A heritability-based comparison of methods used to cluster 16S rRNA gene sequences into operational taxonomic units
A variety of methods are available to collapse 16S rRNA gene sequencing reads to the operational taxonomic units (OTUs) used in microbiome analyses. A number of studies have aimed to compare the quality of the resulting OTUs. However, in the absence of a standard method to define and enumerate the d...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5012273/ https://www.ncbi.nlm.nih.gov/pubmed/27635321 http://dx.doi.org/10.7717/peerj.2341 |
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author | Jackson, Matthew A. Bell, Jordana T. Spector, Tim D. Steves, Claire J. |
author_facet | Jackson, Matthew A. Bell, Jordana T. Spector, Tim D. Steves, Claire J. |
author_sort | Jackson, Matthew A. |
collection | PubMed |
description | A variety of methods are available to collapse 16S rRNA gene sequencing reads to the operational taxonomic units (OTUs) used in microbiome analyses. A number of studies have aimed to compare the quality of the resulting OTUs. However, in the absence of a standard method to define and enumerate the different taxa within a microbial community, existing comparisons have been unable to compare the ability of clustering methods to generate units that accurately represent functional taxonomic segregation. We have previously demonstrated heritability of the microbiome and we propose this as a measure of each methods’ ability to generate OTUs representing biologically relevant units. Our approach assumes that OTUs that best represent the functional units interacting with the hosts’ properties will produce the highest heritability estimates. Using 1,750 unselected individuals from the TwinsUK cohort, we compared 11 approaches to OTU clustering in heritability analyses. We find that de novo clustering methods produce more heritable OTUs than reference based approaches, with VSEARCH and SUMACLUST performing well. We also show that differences resulting from each clustering method are minimal once reads are collapsed by taxonomic assignment, although sample diversity estimates are clearly influenced by OTU clustering approach. These results should help the selection of sequence clustering methods in future microbiome studies, particularly for studies of human host-microbiome interactions. |
format | Online Article Text |
id | pubmed-5012273 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-50122732016-09-15 A heritability-based comparison of methods used to cluster 16S rRNA gene sequences into operational taxonomic units Jackson, Matthew A. Bell, Jordana T. Spector, Tim D. Steves, Claire J. PeerJ Bioinformatics A variety of methods are available to collapse 16S rRNA gene sequencing reads to the operational taxonomic units (OTUs) used in microbiome analyses. A number of studies have aimed to compare the quality of the resulting OTUs. However, in the absence of a standard method to define and enumerate the different taxa within a microbial community, existing comparisons have been unable to compare the ability of clustering methods to generate units that accurately represent functional taxonomic segregation. We have previously demonstrated heritability of the microbiome and we propose this as a measure of each methods’ ability to generate OTUs representing biologically relevant units. Our approach assumes that OTUs that best represent the functional units interacting with the hosts’ properties will produce the highest heritability estimates. Using 1,750 unselected individuals from the TwinsUK cohort, we compared 11 approaches to OTU clustering in heritability analyses. We find that de novo clustering methods produce more heritable OTUs than reference based approaches, with VSEARCH and SUMACLUST performing well. We also show that differences resulting from each clustering method are minimal once reads are collapsed by taxonomic assignment, although sample diversity estimates are clearly influenced by OTU clustering approach. These results should help the selection of sequence clustering methods in future microbiome studies, particularly for studies of human host-microbiome interactions. PeerJ Inc. 2016-08-30 /pmc/articles/PMC5012273/ /pubmed/27635321 http://dx.doi.org/10.7717/peerj.2341 Text en ©2016 Jackson et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Jackson, Matthew A. Bell, Jordana T. Spector, Tim D. Steves, Claire J. A heritability-based comparison of methods used to cluster 16S rRNA gene sequences into operational taxonomic units |
title | A heritability-based comparison of methods used to cluster 16S rRNA gene sequences into operational taxonomic units |
title_full | A heritability-based comparison of methods used to cluster 16S rRNA gene sequences into operational taxonomic units |
title_fullStr | A heritability-based comparison of methods used to cluster 16S rRNA gene sequences into operational taxonomic units |
title_full_unstemmed | A heritability-based comparison of methods used to cluster 16S rRNA gene sequences into operational taxonomic units |
title_short | A heritability-based comparison of methods used to cluster 16S rRNA gene sequences into operational taxonomic units |
title_sort | heritability-based comparison of methods used to cluster 16s rrna gene sequences into operational taxonomic units |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5012273/ https://www.ncbi.nlm.nih.gov/pubmed/27635321 http://dx.doi.org/10.7717/peerj.2341 |
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