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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Bibliographic Details
Main Authors:	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
Format:	article
Language:	EN
Published:	Nature Portfolio 2019
Subjects:	Science Q
Online Access:	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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Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences

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