School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA.
Genomics. 2010 Mar;95(3):143-50. doi: 10.1016/j.ygeno.2009.12.006. Epub 2010 Jan 6.
Identification of transcription factor (TF) activities associated with a certain physiological/experimental condition is one of the preliminary steps to reconstruct transcriptional regulatory networks and to identify signal transduction pathways. TF activities are often indicated by the activities of its target genes. Existing studies on identifying TF activities through target genes usually assume the equivalence between co-regulation and co-expression. However, genes with correlated expression profiles may not be co-regulated. In the mean time, although multiple TFs can be activated coordinately, there is a lack of efficient methods to identify coordinately activated TFs. In this paper, we propose an efficient algorithm embedding a dynamic programming procedure to identify a subset of TFs that are potentially coordinately activated under a given condition by utilizing ranked lists of differentially expressed target genes. Applying our algorithm to microarray expression data sets for a number of diseases, our approach found subsets of TFs that are highly likely associated with the given disease processes.
鉴定与特定生理/实验条件相关的转录因子 (TF) 活性是重建转录调控网络和识别信号转导途径的初步步骤之一。TF 活性通常由其靶基因的活性来表示。通过靶基因识别 TF 活性的现有研究通常假设共调节和共表达之间的等价性。然而，具有相关表达谱的基因可能不是共调节的。同时，尽管多个 TF 可以协调激活，但缺乏有效方法来识别协调激活的 TF。在本文中，我们提出了一种有效的算法，通过利用差异表达靶基因的排序列表，嵌入一个动态规划程序，来识别在给定条件下可能协调激活的一组 TF。将我们的算法应用于多种疾病的微阵列表达数据集，我们的方法发现了与给定疾病过程高度相关的 TF 子集。
