Discriminative motif analysis of high-throughput dataset

Motivation: High-throughput ChIP-seq studies typically identify thousands of peaks for a single transcription factor (TF). It is common for traditional motif discovery tools to predict motifs that are statistically significant against a naive background distribution but are of questionable biological relevance. Results: We describe a simple yet effective algorithm for discovering differential motifs between two sequence datasets that is effective in eliminating systematic biases and scalable to large datasets. Tested on 207 ENCODE ChIP-seq datasets, our method identifies correct motifs in 78% of the datasets with known motifs, demonstrating improvement in both accuracy and efficiency compared with DREME, another state-of-art discriminative motif discovery tool. More interestingly, on the remaining more challenging datasets, we identify common technical or biological factors that compromise the motif search results and use advanced features of our tool to control for these factors. We also present case studies demonstrating the ability of our method to detect single base pair differences in DNA specificity of two similar TFs. Lastly, we demonstrate discovery of key TF motifs involved in tissue specification by examination of high-throughput DNase accessibility data.