Statistical methods for characterizing diversity of microbial communities by analysis of terminal restriction fragment length polymorphisms of 16S rRNA genes
Statistical methods for characterizing diversity of microbial communities by analysis of terminal restriction fragment length polymorphisms of 16S rRNA genes
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DOI：
10.1111/j.1462-2920.2005.00959.x
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发表时间：
2006-05-01
影响因子：
5.1
通讯作者：
Joyce, P
中科院分区：
文献类型：
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作者：
Abdo, Z;Schüette, UME;Joyce, P
16S rRNA 基因的末端限制性片段长度多态性 (T-RFLP) 分析已被证明是比较微生物群落并推测鉴定丰富成员的简便方法。该方法提供可用于根据相似性或距离度量来比较不同社区的数据。一旦群落被聚类成组，就可以从代表每个组的样本中制备克隆文库，以确定群落中数量丰富的种群的系统发育。本文介绍了 T-RFLP 数据统计分析的方法，包括用于 (i) 确定基线以便识别电泳图中“真实”峰的客观方法； (ii) 比较电泳图谱和相似大小的箱片段的方法； (iii) 可用于识别彼此相似的社区的聚类算法； (iv) 选择代表可用于构建 16S rRNA 基因克隆文库的簇的样品的方法。使用具有与实际数据相对应的假设和参数的模拟数据来测试数据分析方法。模拟结果证明了这些方法的有用性，能够恢复在假设下生成的真实微生物群落结构。用于实现这些方法的软件可在 http://www.ibest.uidaho.edu/tools/trflp_stats/index.php 上找到。
The analysis of terminal restriction fragment length polymorphisms (T-RFLP) of 16S rRNA genes has proven to be a facile means to compare microbial communities and presumptively identify abundant members. The method provides data that can be used to compare different communities based on similarity or distance measures. Once communities have been clustered into groups, clone libraries can be prepared from sample(s) that are representative of each group in order to determine the phylogeny of the numerically abundant populations in a community. In this paper methods are introduced for the statistical analysis of T-RFLP data that include objective methods for (i) determining a baseline so that 'true' peaks in electropherograms can be identified; (ii) a means to compare electropherograms and bin fragments of similar size; (iii) clustering algorithms that can be used to identify communities that are similar to one another; and (iv) a means to select samples that are representative of a cluster that can be used to construct 16S rRNA gene clone libraries. The methods for data analysis were tested using simulated data with assumptions and parameters that corresponded to actual data. The simulation results demonstrated the usefulness of these methods in their ability to recover the true microbial community structure generated under the assumptions made. Software for implementing these methods is available at http://www.ibest.uidaho.edu/tools/trflp_stats/index.php.