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 naïve 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. AVAILABILITY: The motifRG package is publically available via the bioconductor repository. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

We previously used simulations of gene expression to demonstrate that rapid activation and deactivation rates stabilized outcomes in stochastic systems. We hypothesized that transient single allele inactivation of an autosomal gene during gametogenesis or very early embryogenesis could have a selective advantage if it permits the functional sampling of each allele and precludes committing maternal effort to an embryo with a deleterious mutation. To test this hypothesis, we simulated the evolution of gene expression activation and deactivation rates and imposed two different selective pressures on the populations: (a). late selection against individuals that cannot maintain a threshold level of gene product that occurs after the investment of maternal effort (i.e., after birth); or (b). early selection: in addition to late selection, maintenance of the gene product above a threshold level was necessary for early development prior to commitment of maternal effort. We found that the opportunity to save reproductive effort from early selection caused the evolution of higher deactivation rates and lower activation rates than in the late selection condition. Thus, we predict that in the special case where early selection can save maternal investment in non-viable offspring, gene expression activation rates and deactivation rates might be selected to permit sampling of the product from each allele.