Method
Evidence-ranked motif identification
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* Corresponding author: Uwe Ohler uwe.ohler@duke.edu
1 Program for Computational Biology and Bioinformatics, Duke University, 102 North Building, Durham, NC 27708, USA
2 Institute for Genome Sciences and Policy, Duke University, 101 Science Drive, Durham, NC 27708, USA
3 Department of Computer Science, Duke University, 450 Research Drive, Durham, NC 27708, USA
4 Department of Statistical Science, Duke University, 214 Old Chemistry Building, Durham, NC 27708, USA
5 Mathematics Department, Duke University, 102 Science Drive, Durham, NC 27708, USA
6 Department of Biostatistics and Bioinformatics, Duke University, Duke University School of Medicine, 2424 Erwin Road, Durham NC 27710, USA
Genome Biology 2010, 11:R19 doi:10.1186/gb-2010-11-2-r19
Published: 15 February 2010Abstract
cERMIT is a computationally efficient motif discovery tool based on analyzing genome-wide quantitative regulatory evidence. Instead of pre-selecting promising candidate sequences, it utilizes information across all sequence regions to search for high-scoring motifs. We apply cERMIT on a range of direct binding and overexpression datasets; it substantially outperforms state-of-the-art approaches on curated ChIP-chip datasets, and easily scales to current mammalian ChIP-seq experiments with data on thousands of non-coding regions.