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

Journal Article · · Frontiers in Microbiology
 [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
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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.

Research Organization:
The Univ. of Texas MD Anderson Cancer Center, Houston, TX (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
1427603
Journal Information:
Frontiers in Microbiology, Vol. 9; ISSN 1664-302X
Publisher:
Frontiers Research FoundationCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 10 works
Citation information provided by
Web of Science

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Cited By (1)

Understanding the Mechanisms Behind the Response to Environmental Perturbation in Microbial Mats: A Metagenomic-Network Based Approach journal November 2018

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Related Subjects

59 BASIC BIOLOGICAL SCIENCES
54 ENVIRONMENTAL SCIENCES
metagenome
microbiome
unsupervised analysis
alpha and beta diversity
sparse data
Anets
qualitative data