Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences

Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences

Obtaining metabolomic data from microbial communities can be costly and difficult, whereas many microbial community sequence datasets are already available. Here Mallick et al. describe a computational approach to predict metabolic features from microbial DNA sequencing information.

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Autores principales: Himel Mallick, Eric A. Franzosa, Lauren J. Mclver, Soumya Banerjee, Alexandra Sirota-Madi, Aleksandar D. Kostic, Clary B. Clish, Hera Vlamakis, Ramnik J. Xavier, Curtis Huttenhower
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2019
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Acceso en línea:https://doaj.org/article/953511452d6847d9a93027a58764b2b1
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spelling oai:doaj.org-article:953511452d6847d9a93027a58764b2b12021-12-02T14:39:16ZPredictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences10.1038/s41467-019-10927-12041-1723https://doaj.org/article/953511452d6847d9a93027a58764b2b12019-07-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-10927-1https://doaj.org/toc/2041-1723Obtaining metabolomic data from microbial communities can be costly and difficult, whereas many microbial community sequence datasets are already available. Here Mallick et al. describe a computational approach to predict metabolic features from microbial DNA sequencing information.Himel MallickEric A. FranzosaLauren J. MclverSoumya BanerjeeAlexandra Sirota-MadiAleksandar D. KosticClary B. ClishHera VlamakisRamnik J. XavierCurtis HuttenhowerNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-11 (2019)
institution DOAJ
collection DOAJ
language EN
topic Science
Q
spellingShingle Science
Q
Himel Mallick
Eric A. Franzosa
Lauren J. Mclver
Soumya Banerjee
Alexandra Sirota-Madi
Aleksandar D. Kostic
Clary B. Clish
Hera Vlamakis
Ramnik J. Xavier
Curtis Huttenhower
Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences
description Obtaining metabolomic data from microbial communities can be costly and difficult, whereas many microbial community sequence datasets are already available. Here Mallick et al. describe a computational approach to predict metabolic features from microbial DNA sequencing information.
format article
author Himel Mallick
Eric A. Franzosa
Lauren J. Mclver
Soumya Banerjee
Alexandra Sirota-Madi
Aleksandar D. Kostic
Clary B. Clish
Hera Vlamakis
Ramnik J. Xavier
Curtis Huttenhower
author_facet Himel Mallick
Eric A. Franzosa
Lauren J. Mclver
Soumya Banerjee
Alexandra Sirota-Madi
Aleksandar D. Kostic
Clary B. Clish
Hera Vlamakis
Ramnik J. Xavier
Curtis Huttenhower
author_sort Himel Mallick
title Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences
title_short Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences
title_full Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences
title_fullStr Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences
title_full_unstemmed Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences
title_sort predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences
publisher Nature Portfolio
publishDate 2019
url https://doaj.org/article/953511452d6847d9a93027a58764b2b1
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AT laurenjmclver predictivemetabolomicprofilingofmicrobialcommunitiesusingampliconormetagenomicsequences
AT soumyabanerjee predictivemetabolomicprofilingofmicrobialcommunitiesusingampliconormetagenomicsequences
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