DNA methylation is identified as an elaborate epigenetic element to regulate binding of transcription factor to gene promoter region. With latest highthroughput technology, it is convenient to accurately test methylation level in experiment, which opens a door to investigate how methylation affects transcription factor. A general model is presented to sense methylation effect on transcription factor in a specific cell. In the model, an inverse sigmoid function is adopted to depict effect of DNA methylation to binding ability of transcription factors with two parameters as center C and steepness S. For each transcription factor, the parameters of model can be fixed by analysis of relativity between transcription factor binding scores in promoter regions and gene expression levels. Here three relativity values should be computed while different formula is used to calculate transcription factor binding score. Relativity value A is obtained when transcription factor binding scores are calculated without considering methylation effect. Relativity value B is analyzed from transcription factor binding scores considering methylation effect with the proposed model. On the contrary, normal sigmoid function is used to depict effect of DNA methylation and relativity value C is just calculated with transcription factor binding scores considering methylation effect using such model. For a transcription factor, if relativity value B is found obviously larger than relativity value A and relativity value C is always less than relativity value A, the transcription factor can be figured out to be apparently affected by DNA methylation and the model with optimal fixed parameters can be used to depict the methylation effect. In neuroblastoma cell, with the proposed model, 10 transcriptional factors were found to be apparently affected by methylation of promoter regions which proves the effectiveness of the model. Based on the proposed model, TF binding status in genome promoter region can be presumed to further investigate how a gene is regulated by a specific group of TFs organized in a particular pattern, which should be helpful in building of gene regulation network. Moreover, if it is exactly obtained how methylation exerts quite different effect on the same TF between normal and cancer cells, the proposed method also can be used as a way to search mutant TFs which would be responsible for the cancer. But, as so many factors including DNA methylation are involved in gene transcription process and to investigate regulation mechanism of gene transcription is still a hard job.