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Linking Associations of Rare Low-Abundance Species to Their Environments by Association Networks

Studies of microbial communities by targeted sequencing of rRNA genes lead to recovering numerous rare low-abundance taxa with unknown biological roles. We propose to study associations of such rare organisms with their environments by a computational framework based on transformation of the data in...

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Autores principales: Karpinets, Tatiana V., Gopalakrishnan, Vancheswaran, Wargo, Jennifer, Futreal, Andrew P., Schadt, Christopher W., Zhang, Jianhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5850922/
https://www.ncbi.nlm.nih.gov/pubmed/29563898
http://dx.doi.org/10.3389/fmicb.2018.00297
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author Karpinets, Tatiana V.
Gopalakrishnan, Vancheswaran
Wargo, Jennifer
Futreal, Andrew P.
Schadt, Christopher W.
Zhang, Jianhua
author_facet Karpinets, Tatiana V.
Gopalakrishnan, Vancheswaran
Wargo, Jennifer
Futreal, Andrew P.
Schadt, Christopher W.
Zhang, Jianhua
author_sort Karpinets, Tatiana V.
collection PubMed
description Studies of microbial communities by targeted sequencing of rRNA genes lead to recovering numerous rare low-abundance taxa with unknown biological roles. We propose to study associations of such rare organisms with their environments by a computational framework based on transformation of the data into qualitative variables. Namely, we analyze the sparse table of putative species or OTUs (operational taxonomic units) and samples generated in such studies, also known as an OTU table, by collecting statistics on co-occurrences of the species and on shared species richness across samples. Based on the statistics we built two association networks, of the rare putative species and of the samples respectively, using a known computational technique, Association networks (Anets) developed for analysis of qualitative data. Clusters of samples and clusters of OTUs are then integrated and combined with metadata of the study to produce a map of associated putative species in their environments. We tested and validated the framework on two types of microbiomes, of human body sites and that of the Populus tree root systems. We show that in both studies the associations of OTUs can separate samples according to environmental or physiological characteristics of the studied systems.
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spelling pubmed-58509222018-03-21 Linking Associations of Rare Low-Abundance Species to Their Environments by Association Networks Karpinets, Tatiana V. Gopalakrishnan, Vancheswaran Wargo, Jennifer Futreal, Andrew P. Schadt, Christopher W. Zhang, Jianhua Front Microbiol Microbiology Studies of microbial communities by targeted sequencing of rRNA genes lead to recovering numerous rare low-abundance taxa with unknown biological roles. We propose to study associations of such rare organisms with their environments by a computational framework based on transformation of the data into qualitative variables. Namely, we analyze the sparse table of putative species or OTUs (operational taxonomic units) and samples generated in such studies, also known as an OTU table, by collecting statistics on co-occurrences of the species and on shared species richness across samples. Based on the statistics we built two association networks, of the rare putative species and of the samples respectively, using a known computational technique, Association networks (Anets) developed for analysis of qualitative data. Clusters of samples and clusters of OTUs are then integrated and combined with metadata of the study to produce a map of associated putative species in their environments. We tested and validated the framework on two types of microbiomes, of human body sites and that of the Populus tree root systems. We show that in both studies the associations of OTUs can separate samples according to environmental or physiological characteristics of the studied systems. Frontiers Media S.A. 2018-03-07 /pmc/articles/PMC5850922/ /pubmed/29563898 http://dx.doi.org/10.3389/fmicb.2018.00297 Text en Copyright © 2018 Karpinets, Gopalakrishnan, Wargo, Futreal, Schadt and Zhang. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
Karpinets, Tatiana V.
Gopalakrishnan, Vancheswaran
Wargo, Jennifer
Futreal, Andrew P.
Schadt, Christopher W.
Zhang, Jianhua
Linking Associations of Rare Low-Abundance Species to Their Environments by Association Networks
title Linking Associations of Rare Low-Abundance Species to Their Environments by Association Networks
title_full Linking Associations of Rare Low-Abundance Species to Their Environments by Association Networks
title_fullStr Linking Associations of Rare Low-Abundance Species to Their Environments by Association Networks
title_full_unstemmed Linking Associations of Rare Low-Abundance Species to Their Environments by Association Networks
title_short Linking Associations of Rare Low-Abundance Species to Their Environments by Association Networks
title_sort linking associations of rare low-abundance species to their environments by association networks
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5850922/
https://www.ncbi.nlm.nih.gov/pubmed/29563898
http://dx.doi.org/10.3389/fmicb.2018.00297
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