Information for regulating a gene's transcription is contained in the conserved patterns (motifs) on the upstream/downstream DNA sequence (promoter region) close to the target gene. By combining the information contained in both gene expression measurements and genes' promoter sequences, I proposed a novel procedure for identifying functional active motifs under certain stimuli. A nonlinear regression model, single index model, was used to associate promoter sequence information of a gene and its mRNA expression measurements. Single index models postulate that the response variable y depends on a unique linear combination of predictors X through an unknown link function f:  y = f(Xβ, ε), where β is the index vector and ε represents measurement errors. In this talk, I will describe computational efficient variable selection procedures and criteria, which were developed by us under profile likelihood frameworks for the single index model. I will also demonstrate the advantage of these methods both theoretically and empirically. Compared with existing methods, our proposed procedures can greatly improve variable selection sensitivities and specificities.