Cargando…

Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences

Profiling phylogenetic marker genes, such as the 16S rRNA gene, is a key tool for studies of microbial communities but does not provide direct evidence of a community’s functional capabilities. Here we describe PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States...

Descripción completa

Detalles Bibliográficos
Autores principales: Langille, Morgan G. I., Zaneveld, Jesse, Caporaso, J. Gregory, McDonald, Daniel, Knights, Dan, Reyes, Joshua A., Clemente, Jose C., Burkepile, Deron E., Vega Thurber, Rebecca L., Knight, Rob, Beiko, Robert G., Huttenhower, Curtis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3819121/
https://www.ncbi.nlm.nih.gov/pubmed/23975157
http://dx.doi.org/10.1038/nbt.2676
_version_ 1782289950548426752
author Langille, Morgan G. I.
Zaneveld, Jesse
Caporaso, J. Gregory
McDonald, Daniel
Knights, Dan
Reyes, Joshua A.
Clemente, Jose C.
Burkepile, Deron E.
Vega Thurber, Rebecca L.
Knight, Rob
Beiko, Robert G.
Huttenhower, Curtis
author_facet Langille, Morgan G. I.
Zaneveld, Jesse
Caporaso, J. Gregory
McDonald, Daniel
Knights, Dan
Reyes, Joshua A.
Clemente, Jose C.
Burkepile, Deron E.
Vega Thurber, Rebecca L.
Knight, Rob
Beiko, Robert G.
Huttenhower, Curtis
author_sort Langille, Morgan G. I.
collection PubMed
description Profiling phylogenetic marker genes, such as the 16S rRNA gene, is a key tool for studies of microbial communities but does not provide direct evidence of a community’s functional capabilities. Here we describe PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States), a computational approach to predict the functional composition of a metagenome using marker gene data and a database of reference genomes. PICRUSt uses an extended ancestral-state reconstruction algorithm to predict which gene families are present and then combines gene families to estimate the composite metagenome. Using 16S information, PICRUSt recaptures key findings from the Human Microbiome Project and accurately predicts the abundance of gene families in host-associated and environmental communities, with quantifiable uncertainty. Our results demonstrate that phylogeny and function are sufficiently linked that this ‘predictive metagenomic’ approach should provide useful insights into the thousands of uncultivated microbial communities for which only marker gene surveys are currently available.
format Online
Article
Text
id pubmed-3819121
institution National Center for Biotechnology Information
language English
publishDate 2013
record_format MEDLINE/PubMed
spelling pubmed-38191212014-03-01 Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences Langille, Morgan G. I. Zaneveld, Jesse Caporaso, J. Gregory McDonald, Daniel Knights, Dan Reyes, Joshua A. Clemente, Jose C. Burkepile, Deron E. Vega Thurber, Rebecca L. Knight, Rob Beiko, Robert G. Huttenhower, Curtis Nat Biotechnol Article Profiling phylogenetic marker genes, such as the 16S rRNA gene, is a key tool for studies of microbial communities but does not provide direct evidence of a community’s functional capabilities. Here we describe PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States), a computational approach to predict the functional composition of a metagenome using marker gene data and a database of reference genomes. PICRUSt uses an extended ancestral-state reconstruction algorithm to predict which gene families are present and then combines gene families to estimate the composite metagenome. Using 16S information, PICRUSt recaptures key findings from the Human Microbiome Project and accurately predicts the abundance of gene families in host-associated and environmental communities, with quantifiable uncertainty. Our results demonstrate that phylogeny and function are sufficiently linked that this ‘predictive metagenomic’ approach should provide useful insights into the thousands of uncultivated microbial communities for which only marker gene surveys are currently available. 2013-08-25 2013-09 /pmc/articles/PMC3819121/ /pubmed/23975157 http://dx.doi.org/10.1038/nbt.2676 Text en http://www.nature.com/authors/editorial_policies/license.html#terms Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Langille, Morgan G. I.
Zaneveld, Jesse
Caporaso, J. Gregory
McDonald, Daniel
Knights, Dan
Reyes, Joshua A.
Clemente, Jose C.
Burkepile, Deron E.
Vega Thurber, Rebecca L.
Knight, Rob
Beiko, Robert G.
Huttenhower, Curtis
Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences
title Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences
title_full Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences
title_fullStr Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences
title_full_unstemmed Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences
title_short Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences
title_sort predictive functional profiling of microbial communities using 16s rrna marker gene sequences
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3819121/
https://www.ncbi.nlm.nih.gov/pubmed/23975157
http://dx.doi.org/10.1038/nbt.2676
work_keys_str_mv AT langillemorgangi predictivefunctionalprofilingofmicrobialcommunitiesusing16srrnamarkergenesequences
AT zaneveldjesse predictivefunctionalprofilingofmicrobialcommunitiesusing16srrnamarkergenesequences
AT caporasojgregory predictivefunctionalprofilingofmicrobialcommunitiesusing16srrnamarkergenesequences
AT mcdonalddaniel predictivefunctionalprofilingofmicrobialcommunitiesusing16srrnamarkergenesequences
AT knightsdan predictivefunctionalprofilingofmicrobialcommunitiesusing16srrnamarkergenesequences
AT reyesjoshuaa predictivefunctionalprofilingofmicrobialcommunitiesusing16srrnamarkergenesequences
AT clementejosec predictivefunctionalprofilingofmicrobialcommunitiesusing16srrnamarkergenesequences
AT burkepilederone predictivefunctionalprofilingofmicrobialcommunitiesusing16srrnamarkergenesequences
AT vegathurberrebeccal predictivefunctionalprofilingofmicrobialcommunitiesusing16srrnamarkergenesequences
AT knightrob predictivefunctionalprofilingofmicrobialcommunitiesusing16srrnamarkergenesequences
AT beikorobertg predictivefunctionalprofilingofmicrobialcommunitiesusing16srrnamarkergenesequences
AT huttenhowercurtis predictivefunctionalprofilingofmicrobialcommunitiesusing16srrnamarkergenesequences