S-palmitoylation is a key regulatory mechanism controlling protein targeting, localization, stability and activity.Since increasing evidence shows that its disruption is implicated in many human diseases, the identification of palmitoylated proteins and the corresponding sites is attracting significant attention.Here we present an in silico identification method for S-palmitoylation sites called SeqPalm based on a series of newly composed features which are derived from protein primary sequence.Using the most comprehensive dataset up to now, this approach achieves a favorable prediction performance, with the corresponding sensitivity, specificity, accuracy, Matthews Correlation Coefficient (MCC) and area under ROC curve(AUC) by 10-fold cross validation of 0.9589, 0.9729, 0.9702, 0.8980 and 0.9986, respectively.The MCC value of this model has achieved the most significant improvement of 14% compared to published predictors on cross-validation of training set and 10% on the same external validation set.Furthermore, the SeqPalm method is applied to the known disease associated variations, so as to get the possible loss or gain of palmitoylation status through these variations.It presents a new approach to discover the molecular basis of pathogenesis which are associated with abnormal palmitoylation.The analyzed result presents several potential therapeutic targets for inherited diseases associated with loss or gain of palmitoylation function.The online server for SeqPalm method is available at http://lishuyan.lzu.edu.cn/seqplam/.