A general approach for discriminative de novo motif discovery from high-throughput data.
De novo motif discovery has been an important challenge of bioinformatics for the past two decades. Since the emergence of high-throughput techniques like ChIP-seq, ChIP-exo and protein-binding microarrays (PBMs), the focus of de novo motif discovery has shifted to runtime and accuracy on large data sets. For this purpose, specialized algorithms have been designed for discovering motifs in ChIP-seq or PBM data. However, none of the existing approaches work perfectly for all three high-throughput techniques. In this article, we propose Dimont, a general approach for fast and accurate de novo motif discovery from high-throughput data. We demonstrate that Dimont yields a higher number of correct motifs from ChIP-seq data than any of the specialized approaches and achieves a higher accuracy for predicting PBM intensities from probe sequence than any of the approaches specifically designed for that purpose. Dimont also reports the expected motifs for several ChIP-exo data sets. Investigating differences between in vitro and in vivo binding, we find that for most transcription factors, the motifs discovered by Dimont are in good accordance between techniques, but we also find notable exceptions. We also observe that modeling intra-motif dependencies may increase accuracy, which indicates that more complex motif models are a worthwhile field of research.
Institute of Computer Science, Martin Luther University Halle-Wittenberg, D-06099 Halle, Saale, Germany, Institute for Biosafety in Plant Biotechnology, Julius Kühn-Institut (JKI) - Federal Research Centre for Cultivated Plants, D-06484 Quedlinburg, Germany and Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466 Seeland OT Gatersleben, Germany.
Jan
J
Grau
Stefan
S
Posch
Ivo
I
Grosse
Jens
J
Keilwagen
D047369:Chromatin Immunoprecipitation / Q000379:methods*; D004247:DNA / Q000737:chemistry*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D059372:Nucleotide Motifs; D040081:Protein Array Analysis / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software
D004247:DNA