转录因子结合模体的贝叶斯聚类
Bayesian Clustering of Transcription Factor Binding Motifs
摘要：
在活细胞中，基因通常由称为转录因子（TF）的蛋白质调节，这些转录因子直接与特定基因附近的短DNA片段结合。这些结合位点具有保守的核苷酸外观，称为基序。最近的几项转录调控研究需要减少大量的基序到集群的基础上，他们的核苷酸组成的相似性。我们提出了一个原则性的方法，这个聚类问题的基础上贝叶斯层次模型，占内和之间的主题的变化。我们使用的狄利克雷过程先验分布，允许集群的数量变化，我们还提出了一种新的概括，允许每个图案的核心宽度变化。该聚类模型的实现，使用吉布斯采样策略，在几个集合的转录因子基序矩阵。我们的集群提供了一种方法，通过它来组织转录因子的基础上结合基序的相似性，这可以用来减少基序冗余的大型数据库，如JASPAR和TRANSFAC。最后，我们的聚类程序已被用于结合发现的进化保守的图案来预测共调节基因。我们的狄利克雷过程先验分布的替代方案进行了探索，但没有显示出实质性的差异，我们的数据集的聚类结果。我们的贝叶斯聚类模型的基础上的Dirichlet过程有几个优势，传统的聚类方法，可以使我们的程序适当的和有用的许多聚类应用。
英文摘要：
Genes are often regulated in living cells by proteins called transcription factors (TFs) that bind directly to short segments of DNA in close proximity to specific genes. These binding sites have a conserved nucleotide appearance, which is called a motif. Several recent studies of transcriptional regulation require the reduction of a large collection of motifs into clusters based on the similarity of their nucleotide composition. We present a principled approach to this clustering problem based upon a Bayesian hierarchical model that accounts for both within- and between-motif variability. We use a Dirichlet process prior distribution that allows the number of clusters to vary and we also present a novel generalization that allows the core width of each motif to vary. This clustering model is implemented, using a Gibbs sampling strategy, on several collections of transcription factor motif matrices. Our clusters provide a means by which to organize transcription factors based on binding motif similarities, which can be used to reduce motif redundancy within large databases such as JASPAR and TRANSFAC. Finally, our clustering procedure has been used in combination with discovery of evolutionarily-conserved motifs to predict co-regulated genes. An alternative to our Dirichlet process prior distribution is explored but shows no substantive difference in the clustering results for our datasets. Our Bayesian clustering model based on the Dirichlet process has several advantages over traditional clustering methods that could make our procedure appropriate and useful for many clustering applications.
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