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Molecular ecological network analyses

Journal Article · · BMC Bioinformatics
 [1];  [2];  [3];  [2];  [4];  [5]
  1. Univ. of Oklahoma, Norman, OK (United States). Inst. for Environmental Genomics. Dept. of Botany and Microbiology; Glomics Inc, Norman, OK (United States); DOE/OSTI
  2. Univ. of Oklahoma, Norman, OK (United States). Inst. for Environmental Genomics. Dept. of Botany and Microbiology
  3. Tsinghua Univ., Beijing (China). State Key Joint Lab. of Environment Simulation and Pollution Control. School of Environment
  4. Clemson Univ., SC (United States). School of Computing
  5. Univ. of Oklahoma, Norman, OK (United States). Inst. for Environmental Genomics. Dept. of Botany and Microbiology; Tsinghua Univ., Beijing (China). State Key Joint Lab. of Environment Simulation and Pollution Control. School of Environment; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Earth Sciences Division
Background: Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data. Results: Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA). Conclusions: The RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.
Research Organization:
Glomics, Inc., Norman, OK (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Univ. of Oklahoma, Norman, OK (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division
Grant/Contract Number:
AC02-05CH11231; SC0004601; SC0004613
OSTI ID:
1626287
Journal Information:
BMC Bioinformatics, Journal Name: BMC Bioinformatics Journal Issue: 1 Vol. 13; ISSN 1471-2105
Publisher:
BioMed CentralCopyright Statement
Country of Publication:
United States
Language:
English

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