Molecular ecological network analyses.
Molecular ecological network analyses.
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DOI：
10.1186/1471-2105-13-113
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发表时间：
2012-05-30
影响因子：
3
通讯作者：
Zhou J
中科院分区：
文献类型：
--
作者：
Deng Y;Jiang YH;Yang Y;He Z;Luo F;Zhou J
了解群落内不同物种之间的相互作用及其对环境变化的反应是生态学的中心目标。然而，定义微生物群落中的网络结构是非常具有挑战性的，因为它们具有极高的多样性和迄今尚未培养的状态。尽管高通量测序和功能基因阵列等元基因组技术的最新进展为分析微生物群落结构提供了革命性的工具，但基于高通量元基因组数据来研究微生物群落中的网络相互作用仍然是困难的。在这里，我们描述了一个新的数学和生物信息学框架，通过基于随机矩阵理论(RMT)的方法来构建生态关联网络--分子生态网络(MENS)。与其他网络构建方法相比，该方法的显著之处在于网络是自动定义的，并且对噪声具有健壮性，从而为与高通量元基因组数据相关的几个常见问题提供了极好的解决方案。我们基于16个 S rRNA基因的焦磷酸测序数据，应用该方法确定了长期实验变暖下微生物群落的网络结构。结果表明，在变暖和不变暖条件下构建的MAN都表现出无标度、小世界和模块化的拓扑特征，这与以前描述的分子生态网络是一致的。特征分析表明，特征能较好地代表模数分布。与许多其他研究一致的是，包括温度和土壤pH在内的几个主要环境特征被发现在确定所研究的微生物群落中的网络相互作用方面非常重要。为了方便科学界的应用，所有这些方法和统计工具已经集成到一个全面的分子生态网络分析管道(MENAP)中，该管道现在是开放访问的(http://ieg2.ou.edu/MENA).基于RMT的分子生态网络分析为阐明微生物群落中的网络相互作用及其对环境变化的响应提供了强有力的工具，这对微生物生态学和环境微生物学的研究具有重要意义。
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. 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). 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.
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影响因子：
64.8
作者：
Arumugam, Manimozhiyan;Raes, Jeroen;Pelletier, Eric;Le Paslier, Denis;Yamada, Takuji;Mende, Daniel R.;Fernandes, Gabriel R.;Tap, Julien;Bruls, Thomas;Batto, Jean-Michel;Bertalan, Marcelo;Borruel, Natalia;Casellas, Francesc;Fernandez, Leyden;Gautier, Laurent;Hansen, Torben;Hattori, Masahira;Hayashi, Tetsuya;Kleerebezem, Michiel;Kurokawa, Ken;Leclerc, Marion;Levenez, Florence;Manichanh, Chaysavanh;Nielsen, H. Bjorn;Nielsen, Trine;Pons, Nicolas;Poulain, Julie;Qin, Junjie;Sicheritz-Ponten, Thomas;Tims, Sebastian;Torrents, David;Ugarte, Edgardo;Zoetendal, Erwin G.;Wang, Jun;Guarner, Francisco;Pedersen, Oluf;de Vos, Willem M.;Brunak, Soren;Dore, Joel;Weissenbach, Jean;Ehrlich, S. Dusko;Bork, Peer
通讯作者：
Bork, Peer
影响因子：
4.4
作者：
BONACICH, P
通讯作者：
BONACICH, P
DOI：
10.1073/pnas.97.18.10101
发表时间：
2000-08-29
影响因子：
11.1
作者：
Alter, O;Brown, PO;Botstein, D
通讯作者：
Botstein, D
影响因子：
56.9
作者：
Barabási, AL;Albert, R
通讯作者：
Albert, R
影响因子：
7
作者：
Chaffron, Samuel;Rehrauer, Hubert;von Mering, Christian
通讯作者：
von Mering, Christian