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

ABSTRACT: 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 fo

SUBMITTER: Grau J 

PROVIDER: S-EPMC3834837 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature