序列的概率聚类：通过比较基因组学推断新的细菌调节子
Probabilistic Clustering of Sequences: Inferring new bacterial regulons by comparative genomics
摘要：
肠道细菌之间的全基因组比较在基因基础上产生大量保守的推定调控位点，这些位点需要聚集成调节子。使用的假设，监管网站可以表示为样本的权重矩阵，我们得到一个独特的概率分布分配到集群的网站。我们的算法，“序列的概率聚类”，使用蒙特卡罗抽样的分布分区和排列成集群的短DNA序列数以千计。该算法从内部确定数据中的聚类数，并为结果聚类分配重要性。我们把理论上的限制，任何算法的能力，正确聚类序列的权重矩阵（WM）时，这些WM是未知的。我们的分析表明，一个单一基因组的所有推定位点的集合（例如E。coli）在很大程度上不足以进行聚类。当来自不同基因组的位点被组合起来，并且来自不同物种的所有同源位点被用作一个区块时，聚类就变得可行了。我们预测50-100个新的调节子以及现有调节子的许多新成员，可能使E.杆菌
英文摘要：
Genome wide comparisons between enteric bacteria yield large sets of conserved putative regulatory sites on a gene by gene basis that need to be clustered into regulons. Using the assumption that regulatory sites can be represented as samples from weight matrices we derive a unique probability distribution for assignments of sites into clusters. Our algorithm, 'PROCSE' (probabilistic clustering of sequences), uses Monte-Carlo sampling of this distribution to partition and align thousands of short DNA sequences into clusters. The algorithm internally determines the number of clusters from the data, and assigns significance to the resulting clusters. We place theoretical limits on the ability of any algorithm to correctly cluster sequences drawn from weight matrices (WMs) when these WMs are unknown. Our analysis suggests that the set of all putative sites for a single genome (e.g. E. coli) is largely inadequate for clustering. When sites from different genomes are combined and all the homologous sites from the various species are used as a block, clustering becomes feasible. We predict 50-100 new regulons as well as many new members of existing regulons, potentially doubling the number of known regulatory sites in E. coli.
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