Rodriguez-Brito Beltran, Rohwer Forest, Edwards Robert A
Computational Science Research Center, San Diego State University, San Diego, USA.
BMC Bioinformatics. 2006 Mar 20;7:162. doi: 10.1186/1471-2105-7-162.
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.
宏基因组学，即对直接从环境中分离出的基因组DNA进行序列分析，可用于识别生物并模拟特定生态系统的群落动态。宏基因组学还有潜力识别不同环境中显著不同的代谢潜能。
在此我们使用一种统计方法来比较经过整理的子系统，以便从宏基因组预测生理学、代谢和生态学。这种方法可用于识别宏基因组序列之间显著不同的那些子系统。与非冗余数据库相比，在马尾藻海和酸性矿山排水宏基因组中过度代表的子系统被识别出来。
本文所述方法将统计学应用于宏基因组中代谢潜能的比较。该分析揭示了在被比较的不同环境中或多或少存在的那些子系统。这些代谢潜能的差异导致了关于这些生态系统中微生物生理学和代谢的几个可检验假设。
