Dimont

De novo motif discovery is a critical task in bioinformatics, as it enables the identification of functional DNA regions that regulate gene expression. With the emergence of high-throughput techniques like ChIP-seq, ChIP-exo, and protein-binding microarrays (PBMs), the need for fast and accurate de novo motif discovery has become increasingly important. However, the existing specialized algorithms for discovering motifs in ChIP-seq or PBM data are imperfect, as they do not work equally for all three high-throughput techniques.

To address this challenge, the authors proposed Dimont, a general approach for fast and accurate de novo motif discovery from high-throughput data. This approach yields a higher number of correct motifs from ChIP-seq data than any of the specialized approaches, and it achieves a higher accuracy for predicting PBM intensities from probe sequence than any of the approaches designed explicitly for that purpose. Additionally, Dimont reports the expected motifs for several ChIP-exo datasets.

The researchers also investigated differences between in vitro and in vivo binding and found that, for most transcription factors, the motifs discovered by Dimont are in good accordance between techniques, with some notable exceptions. Furthermore, the researchers found that modeling intra-motif dependencies can increase accuracy, indicating that more complex motif models are a worthwhile field of research.