Abdo Zaid, Schüette Ursel M E, Bent Stephen J, Williams Christopher J, Forney Larry J, Joyce Paul
Department of Mathematics, University of Idaho, Moscow, ID 83844-1104, USA.
Environ Microbiol. 2006 May;8(5):929-38. doi: 10.1111/j.1462-2920.2005.00959.x.
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.
16S rRNA基因的末端限制性片段长度多态性(T-RFLP)分析已被证明是一种比较微生物群落并推测鉴定优势成员的简便方法。该方法提供的数据可用于基于相似性或距离度量来比较不同的群落。一旦将群落聚类成组,就可以从代表每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数据的统计分析方法,包括用于以下方面的客观方法:(i)确定基线,以便能够识别电泳图中的“真实”峰;(ii)比较电泳图和大小相似片段分类的方法;(iii)可用于识别彼此相似群落的聚类算法;以及(iv)选择可用于构建16S rRNA基因克隆文库的代表一个聚类的样本的方法。使用具有与实际数据相对应的假设和参数的模拟数据对数据分析方法进行了测试。模拟结果证明了这些方法在恢复在所作假设下产生的真实微生物群落结构方面的有用性。可在http://www.ibest.uidaho.edu/tools/trflp_stats/index.php获得实施这些方法的软件。