Simultaneously Learning DNA Motif along with Its Position and Sequence Rank Preferences through EM Algorithm

Although de novo motifs can be discovered through mining over-represented sequence patterns, this approach misses some real motifs and generates many false positives. To improve accuracy, one solution is to consider some additional binding features (i.e. position preference and sequence rank preference). This information is usually required from the user. This paper presents a de novo motif discovery algorithm called SEME which uses pure probabilistic mixture model to model the motif's binding features and uses expectation maximization (EM) algorithms to simultaneously learn the sequence motif, position and sequence rank preferences without asking for any prior knowledge from the user. SEME is both efficient and accurate thanks to two important techniques: the variable motif length extension and importance sampling. Using 75 large scale synthetic datasets, 32 metazoan compendium benchmark datasets and 164 ChIP-Seq libraries, we demonstrated the superior performance of SEME over existing programs in finding transcription factor (TF) binding sites. SEME is further applied to a more difficult problem of finding the co-regulated TF (co-TF) motifs in 15 ChIP-Seq libraries. It identified significantly more correct co-TF motifs and, at the same time, predicted co-TF motifs with better matching to the known motifs. Finally, we show that the learned position and sequence rank preferences of each co-TF reveals potential interaction mechanisms between the primary TF and the co-TF within these sites. Some of these findings were further validated by the ChIP-Seq experiments of the co-TFs.