De novo motif discovery facilitates identification of interactions between transcription factors in Saccharomyces cerevisiae


Gene regulation involves complicated mechanisms such as cooperativity between a set of transcription factors (TFs). Previous studies have used target genes shared by two TFs as a clue to infer TF-TF interactions. However, this task remains challenging because the target genes with low binding affinity are frequently omitted by experimental data, especially when a single strict threshold is employed. This article aims at improving the accuracy of inferring TF-TF interactions by incorporating motif discovery as a fundamental step when detecting overlapping targets of TFs based on ChIP-chip data. The proposed method, simTFBS, outperforms three naïve methods that adopt fixed thresholds when inferring TF-TF interactions based on ChIP-chip data. In addition, simTFBS is compared with two advanced methods and demonstrates its advantages in predicting TF-TF interactions. By comparing simTFBS with predictions based on the set of available annotated yeast TF binding motifs, we demonstrate that the good performance of simTFBS is indeed coming from the additional motifs found by the proposed procedures. Supplementary data are available at Bioinformatics online.

Huai-Kuang Tsai
Huai-Kuang Tsai
Research Fellow/Professor