Abstract
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 R3) 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.
Highlights
Inferring networks of genetic interactions (GIs) from microarray data is one of the challenging tasks in the area of functional genomics
APPLICATION 1: GENETIC NETWORKS OF THE 51 YEAST GENES INVOLVED IN DNA SYNTHESIS AND DNA REPAIR we apply data-driven second order model (DDSOM) and the RS algorithm to infer GIs of 51 yeast genes involved in DNA synthesis and DNA repair
The resulting mode surface of DDSOM is identified by the majority of models fitted well by gene expression data, it can be applied to any data set
Summary
Inferring networks of genetic interactions (GIs) from microarray data is one of the challenging tasks in the area of functional genomics. An inferred genetic network predicts how a given gene interacts with the other genes. Predicting transcriptional compensation (TC; Kafri et al, 2005) and transcriptional diminishment (TD) interactions (Chuang et al, 2008; Shieh et al, 2008) from a pair of SSL genes is of interest. Given a SSL or paralog gene pair, following a gene’s loss, its partner gene’s expression increases; this phenomenon is known as TC. Quantitative RT-polymerase chain reaction (qRT-PCR) experiments (in Appendix) show that besides TC, in some cases following a gene’s absence, its partner gene’s expression decreased; we call this phenomenon TD. TC/TD interactions among a group of 51 yeast genes, involved in DNA synthesis and DNA repair, is of interest to our collaborator, and this motivates us to develop this algorithm
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