Combining phylogenetic motif discovery and motif clustering to predict co-regulated genes
Combining phylogenetic motif discovery and motif clustering to predict co-regulated genes
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DOI：
10.1093/bioinformatics/bti628
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发表时间：
2005-10-15
期刊：
影响因子：
5.8
通讯作者：
Liu, JS
中科院分区：
文献类型：
--
作者：
Jensen, ST;Shen, L;Liu, JS
动机：我们提出了一个基于序列的框架和算法PHYLOCLUS预测共调节基因。在我们的方法中，从头发现的方法被用来找到保守的图案的进化，然后贝叶斯层次聚类模型被用来聚类这些图案，从而分组在一起的基因，puppelco调节。我们的聚类过程允许的集群和每个集群内的基序宽度的数量是unknown.Results：我们使用我们的框架来预测共调节基因的细菌枯草芽孢杆菌使用其他六个密切相关的细菌物种。我们预测的图案和基因簇验证使用几个外部来源和显着的集群进行了详细检查。扩展到发现和聚类的两块基序可以用于推断转录因子之间的协同结合关系。
Motivation: We present a sequence-based framework and algorithm PHYLOCLUS for predicting co-regulated genes. In our approach, de novo discovery methods are used to find motifs conserved by evolution and then a Bayesian hierarchical clustering model is used to cluster these motifs, thereby grouping together genes that are putatively co-regulated. Our clustering procedure allows both the number of clusters and the motif width within each cluster to be unknown.Results: We use our framework to predict co-regulated genes in the bacterium Bacillus subtilis using six other closely related bacterial species. Our predicted motifs and gene clusters are validated using several external sources and significant clusters are examined in detail. An extension to the discovery and clustering of two-block motifs can be used for inference about synergistic binding relationships between transcription factors.