Bayesian estimation of bacterial community composition from 454 sequencing data.
Bayesian estimation of bacterial community composition from 454 sequencing data.
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DOI：
10.1093/nar/gks227
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发表时间：
2012-07
影响因子：
14.9
通讯作者：
Corander J
中科院分区：
文献类型：
--
作者：
Cheng L;Walker AW;Corander J
从不同应用背景下的混合样品中估计细菌群落组成是许多微生物学家的重要任务。细菌群落组成通常通过聚合酶链反应扩增的16 S rRNA基因序列聚类来估计。目前用于分析这些序列的分类学无关的聚类方法，如UCLUST、ESPRIT-Tree和CROP，具有两个局限性：（i）需要专家知识，即需要指定物种之间的差异截止值;（ii）不能分离密切相关的物种。第一个限制给用户带来了负担，因为需要相当大的努力来选择适当的参数，而第二个限制导致对潜在细菌群落组成的不准确描述。我们提出了一种基于概率模型的方法来估计细菌群落组成，解决了这些限制。我们的方法只需要很少的专业知识，只需要指定可能的最大聚类数。此外，我们的方法证明了它的能力，在两个实验中分离密切相关的物种，尽管测序错误和个体变异。
Estimating bacterial community composition from a mixed sample in different applied contexts is an important task for many microbiologists. The bacterial community composition is commonly estimated by clustering polymerase chain reaction amplified 16S rRNA gene sequences. Current taxonomy-independent clustering methods for analyzing these sequences, such as UCLUST, ESPRIT-Tree and CROP, have two limitations: (i) expert knowledge is needed, i.e. a difference cutoff between species needs to be specified; (ii) closely related species cannot be separated. The first limitation imposes a burden on the user, since considerable effort is needed to select appropriate parameters, whereas the second limitation leads to an inaccurate description of the underlying bacterial community composition. We propose a probabilistic model-based method to estimate bacterial community composition which tackles these limitations. Our method requires very little expert knowledge, where only the possible maximum number of clusters needs to be specified. Also our method demonstrates its ability to separate closely related species in two experiments, in spite of sequencing errors and individual variations.
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影响因子：
3
作者：
Cheng L;Connor TR;Aanensen DM;Spratt BG;Corander J
通讯作者：
Corander J
影响因子：
5.8
作者：
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通讯作者：
Godzik, A
影响因子：
4.4
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通讯作者：
Weber, Carolyn F.
影响因子：
14.9
作者：
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通讯作者：
Sun Y
影响因子：
4.4
作者：
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通讯作者：
Cole, James R.