A general approach for discriminative de novo motif discovery from high-throughput data

Abstract

High-throughput techniques like ChIP-seq, ChIP-exo, and protein binding microarrays (PBMs) demand for novel de novo motif discovery approaches that focus on accuracy and runtime on large data sets. While specialized algorithms have been designed for discovering motifs in in-vivo ChIP-seq/ChIP-exo or in in-vitro PBM data, none of these works equally well for all these high-throughput techniques. Here, we present Dimont, a general approach for fast and accurate de-novo motif discovery from high-throughput data, which achieves a competitive performance on both ChIP-seq and PBM data compared to recent approaches specifically designed for either technique. Hence, Dimont allows for investigating differences between in-vitro and in-vivo binding in an unbiased manner using a unified approach. For most transcription factors, Dimont discovers similar motifs from in-vivo and in-vitro data, but we also find notable exceptions. Scrutinizing the benefit of modeling dependencies between binding site positions, we find that more complex motif models often increase prediction performance and, hence, are a worthwhile field of research. Original paper: doi: 10.1093/nar/gkt831