Pati Amrita, Vasquez-Robinet Cecilia, Heath Lenwood S, Grene Ruth, Murali T M
Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA.
BMC Bioinformatics. 2006 Apr 21;7:218. doi: 10.1186/1471-2105-7-218.
Modeling of cis-elements or regulatory motifs in promoter (upstream) regions of genes is a challenging computational problem. In this work, set of regulatory motifs simultaneously present in the promoters of a set of genes is modeled as a biclique in a suitably defined bipartite graph. A biologically meaningful co-occurrence of multiple cis-elements in a gene promoter is assessed by the combined analysis of genomic and gene expression data. Greater statistical significance is associated with a set of genes that shares a common set of regulatory motifs, while simultaneously exhibiting highly correlated gene expression under given experimental conditions.
XcisClique, the system developed in this work, is a comprehensive infrastructure that associates annotated genome and gene expression data, models known cis-elements as regular expressions, identifies maximal bicliques in a bipartite gene-motif graph; and ranks bicliques based on their computed statistical significance. Significance is a function of the probability of occurrence of those motifs in a biclique (a hypergeometric distribution), and on the new sum of absolute values statistic (SAV) that uses Spearman correlations of gene expression vectors. SAV is a statistic well-suited for this purpose as described in the discussion.
XcisClique identifies new motif and gene combinations that might indicate as yet unidentified involvement of sets of genes in biological functions and processes. It currently supports Arabidopsis thaliana and can be adapted to other organisms, assuming the existence of annotated genomic sequences, suitable gene expression data, and identified regulatory motifs. A subset of Xcis Clique functionalities, including the motif visualization component MotifSee, source code, and supplementary material are available at https://bioinformatics.cs.vt.edu/xcisclique/.
对基因启动子（上游）区域中的顺式元件或调控基序进行建模是一个具有挑战性的计算问题。在这项工作中，将一组基因的启动子中同时存在的调控基序集建模为一个适当定义的二分图中的双团。通过对基因组和基因表达数据的综合分析，评估基因启动子中多个顺式元件在生物学上有意义的共现情况。一组共享一组共同调控基序，同时在给定实验条件下表现出高度相关基因表达的基因具有更高的统计显著性。
XcisClique 是在这项工作中开发的系统，是一个综合的基础设施，它关联注释的基因组和基因表达数据，将已知的顺式元件建模为正则表达式，在二分基因 - 基序图中识别最大双团，并根据计算出的统计显著性对双团进行排名。显著性是双团中这些基序出现概率（超几何分布）以及使用基因表达向量的 Spearman 相关性的新绝对值统计量（SAV）之和的函数。如讨论中所述，SAV 是非常适合此目的的统计量。
XcisClique 识别出新的基序和基因组合，这些组合可能表明一组基因在生物学功能和过程中存在尚未确定的参与情况。它目前支持拟南芥，并且假设存在注释的基因组序列、合适的基因表达数据和已识别的调控基序，可适用于其他生物体。Xcis Clique 的一部分功能，包括基序可视化组件 MotifSee、源代码和补充材料可在 https://bioinformatics.cs.vt.edu/xcisclique/ 获得。
