An application of statistics to comparative metagenomics.
An application of statistics to comparative metagenomics.
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DOI：
10.1186/1471-2105-7-162
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发表时间：
2006-03-20
影响因子：
3
通讯作者：
Edwards RA
中科院分区：
文献类型：
--
作者：
Rodriguez-Brito B;Rohwer F;Edwards RA
宏基因组学是对直接从环境中分离的基因组DNA进行序列分析，可用于识别生物体和模拟特定生态系统的群落动态。宏基因组学也有可能在不同的环境中识别出显著不同的代谢潜能。在这里，我们使用统计方法来比较策划子系统，以预测生理，代谢和生态从宏基因组。该方法可用于识别宏基因组序列之间存在显著差异的子系统。与非冗余数据库相比，确定了马尾藻海和酸性矿井排水宏基因组中代表性过高的子系统。本文所描述的方法将统计学应用于宏基因组中代谢潜能的比较。该分析揭示了在比较的不同环境中更多或更少地表示的那些子系统。这些代谢潜能的差异导致了关于这些生态系统中微生物的生理和代谢的几个可测试的假设。
Metagenomics, sequence analyses of genomic DNA isolated directly from the environments, can be used to identify organisms and model community dynamics of a particular ecosystem. Metagenomics also has the potential to identify significantly different metabolic potential in different environments. Here we use a statistical method to compare curated subsystems, to predict the physiology, metabolism, and ecology from metagenomes. This approach can be used to identify those subsystems that are significantly different between metagenome sequences. Subsystems that were overrepresented in the Sargasso Sea and Acid Mine Drainage metagenome when compared to non-redundant databases were identified. The methodology described herein applies statistics to the comparisons of metabolic potential in metagenomes. This analysis reveals those subsystems that are more, or less, represented in the different environments that are compared. These differences in metabolic potential lead to several testable hypotheses about physiology and metabolism of microbes from these ecosystems.
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影响因子：
3.2
作者：
McCarren, J;Heuser, J;Brahamsha, B
通讯作者：
Brahamsha, B
影响因子：
56.9
作者：
Venter, JC;Remington, K;Smith, HO
通讯作者：
Smith, HO
影响因子：
--
作者：
Galinski, E A
通讯作者：
Galinski, E A
影响因子：
56.9
作者：
Wu, JF;Sunda, W;Karl, DM
通讯作者：
Karl, DM
影响因子：
64.8
作者：
Tyson, GW;Chapman, J;Banfield, JF
通讯作者：
Banfield, JF