The prediction of transcription factor binding sites is one of main issues in Biology. Performances of stepwise linear regression, Bayesian additive regression trees, boosting, least angel regression and Dantzig selector with sequential optimization were examined. Prediction accuracy of selecting sequence features for TF-DNA binding were measured by simulation and empirical studies. Least angel regression performed best in this study when being evaluated by its accuracy, stability and computational cost.