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A package based on an algorithm for discovering differential motifs between two sequence datasets that is effective in eliminating systematic biases and scalable to large datasets. motifRG measures the discriminative power of a motif by a logistic regression model, which shows some similarity to DREME and PeakRegressor, but offers a better combination of robustness and flexibility. It also provides an effective and efficient iterative process for motif refinement and extension and applies a bootstrap robustness test to avoid over-fitting in the optimization process. This method, and discriminative motif discovery in general, represents powerful tools to exploit various types of high-throughput datasets to answer many fundamental biological questions.

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motifRG classification

motifRG specifications

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Command line interface
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Unix/Linux, Mac OS, Windows
Artistic License version 2.0

motifRG distribution


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Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA; Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; Department of Pediatrics, School of Medicine, Department of Neurology, School of Medicine, University of Washington, Seattle, WA, USA; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; Department of Computer Science and Engineering, Department of Genome Sciences, University of Washington, Seattle, WA, USA; Bioinformatics and Computational Biology, Genentech, South San Francisco, CA, USA

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