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

Abstract

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.

Authors:
 [1];  [2];  [3];  [4];  [5];  [4]
  1. The Univ. of Texas MD Anderson Cancer Center, Houston, TX (United States). Dept. of Genomic Medicine; Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Biosciences Division
  2. The Univ. of Texas MD Anderson Cancer Center, Houston, TX (United States). Dept. of Surgical Oncology; Univ. of Texas School of Public Health, Dallas, TX (United States). Dept. of Epidemiology, Human Genetics and Environmental Sciences
  3. The Univ. of Texas MD Anderson Cancer Center, Houston, TX (United States). Dept. of Genomic Medicine. Dept. of Surgical Oncology
  4. The Univ. of Texas MD Anderson Cancer Center, Houston, TX (United States). Dept. of Genomic Medicine
  5. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Biosciences Division; Univ. of Tennessee, Knoxville, TN (United States). Dept. of Microbiology
Publication Date:
Research Org.:
The Univ. of Texas MD Anderson Cancer Center, Houston, TX (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1427603
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Frontiers in Microbiology
Additional Journal Information:
Journal Volume: 9; Journal ID: ISSN 1664-302X
Publisher:
Frontiers Research Foundation
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 54 ENVIRONMENTAL SCIENCES; metagenome; microbiome; unsupervised analysis; alpha and beta diversity; sparse data; Anets; qualitative data

Citation Formats

Karpinets, Tatiana V., Gopalakrishnan, Vancheswaran, Wargo, Jennifer, Futreal, Andrew P., Schadt, Christopher W., and Zhang, Jianhua. Linking Associations of Rare Low-Abundance Species to Their Environments by Association Networks. United States: N. p., 2018. Web. doi:10.3389/fmicb.2018.00297.
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. United States. doi:10.3389/fmicb.2018.00297.
Karpinets, Tatiana V., Gopalakrishnan, Vancheswaran, Wargo, Jennifer, Futreal, Andrew P., Schadt, Christopher W., and Zhang, Jianhua. Wed . "Linking Associations of Rare Low-Abundance Species to Their Environments by Association Networks". United States. doi:10.3389/fmicb.2018.00297. https://www.osti.gov/servlets/purl/1427603.
@article{osti_1427603,
title = {Linking Associations of Rare Low-Abundance Species to Their Environments by Association Networks},
author = {Karpinets, Tatiana V. and Gopalakrishnan, Vancheswaran and Wargo, Jennifer and Futreal, Andrew P. and Schadt, Christopher W. and Zhang, Jianhua},
abstractNote = {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.},
doi = {10.3389/fmicb.2018.00297},
journal = {Frontiers in Microbiology},
number = ,
volume = 9,
place = {United States},
year = {2018},
month = {3}
}

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