Jiang Ci-Ren, Hung Ying-Chao, Chen Chung-Ming, Shieh Grace S
Institute of Statistical Science, Academia Sinica Taipei, Taiwan.
Front Genet. 2012 May 3;3:71. doi: 10.3389/fgene.2012.00071. eCollection 2012.
Genetic/transcriptional regulatory interactions are shown to predict partial components of signaling pathways, which have been recognized as vital to complex human diseases. Both activator (A) and repressor (R) are known to coregulate their common target gene (T). Xu et al. (2002) proposed to model this coregulation by a fixed second order response surface (called the RS algorithm), in which T is a function of A, R, and AR. Unfortunately, the RS algorithm did not result in a sufficient number of genetic interactions (GIs) when it was applied to a group of 51 yeast genes in a pilot study. Thus, we propose a data-driven second order model (DDSOM), an approximation to the non-linear transcriptional interactions, to infer genetic and transcriptional regulatory interactions. For each triplet of genes of interest (A, R, and T), we regress the expression of T at time t + 1 on the expression of A, R, and AR at time t. Next, these well-fitted regression models (viewed as points in R(3)) are collected, and the center of these points is used to identify triples of genes having the A-R-T relationship or GIs. The DDSOM and RS algorithms are first compared on inferring transcriptional compensation interactions of a group of yeast genes in DNA synthesis and DNA repair using microarray gene expression data; the DDSOM algorithm results in higher modified true positive rate (about 75%) than that of the RS algorithm, checked against quantitative RT-polymerase chain reaction results. These validated GIs are reported, among which some coincide with certain interactions in DNA repair and genome instability pathways in yeast. This suggests that the DDSOM algorithm has potential to predict pathway components. Further, both algorithms are applied to predict transcriptional regulatory interactions of 63 yeast genes. Checked against the known transcriptional regulatory interactions queried from TRANSFAC, the proposed also performs better than the RS algorithm.
遗传/转录调控相互作用被证明可预测信号通路的部分组成成分，而这些信号通路已被认为对复杂的人类疾病至关重要。已知激活剂（A）和抑制剂（R）共同调节它们的共同靶基因（T）。徐等人（2002年）提出通过固定的二阶响应面（称为RS算法）对这种共同调节进行建模，其中T是A、R和AR的函数。不幸的是，在一项初步研究中，当将RS算法应用于一组51个酵母基因时，并未产生足够数量的遗传相互作用（GI）。因此，我们提出了一种数据驱动的二阶模型（DDSOM），它是对非线性转录相互作用的一种近似，用于推断遗传和转录调控相互作用。对于感兴趣的每三个基因（A、R和T），我们将时间t + 1时T的表达量对时间t时A、R和AR的表达量进行回归分析。接下来，收集这些拟合良好的回归模型（视为R(3)中的点），并使用这些点的中心来识别具有A - R - T关系或GI的基因三元组。首先，使用微阵列基因表达数据，在推断一组酵母基因在DNA合成和DNA修复中的转录补偿相互作用方面，对DDSOM和RS算法进行了比较；与定量RT - 聚合酶链反应结果相比，DDSOM算法的修正真阳性率更高（约75%），高于RS算法。报告了这些经过验证的GI，其中一些与酵母中DNA修复和基因组不稳定途径中的某些相互作用一致。这表明DDSOM算法有预测通路成分的潜力。此外，两种算法都被应用于预测63个酵母基因的转录调控相互作用。与从TRANSFAC查询到的已知转录调控相互作用相比，所提出的算法也比RS算法表现更好。
