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...
Autores principales: | , , , , , , , , , , , |
---|---|
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 |