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PRIORITY is a software package for de novo motif discovery. Its goal is to incorporate informative position-based priors into a collapsed Gibbs sampling algorithm, as developed by Lee Narlikar, Raluca Gordân, Uwe Ohler, and Alex Hartemink (see PRIORITY documentation).

By default, PRIORITY uses class-specific priors derived from three sparse Bayesian classifiers trained to recognize binding sites of three structural classes of transcription factors (TFs): basic leucine zipper, forkhead, and basic helix loop helix. We add a default flat prior to handle TFs of other classes. The three binary classifiers are based on logistic regression and were trained using SMLR.

In addition to discovering locations of TF binding sites and an associated motif, PRIORITY also predicts the structural class of the TF recognizing the identified binding sites.

PRIORITY was developed by Raluca Gordân under the direction of Alexander J. Hartemink in the Department of Computer Science at Duke University. PRIORITY is written in portable Java and thus it is available on any system that has a recent JVM (see PRIORITY requirements).

The current version of PRIORITY is 2.1.0.

Licensing Overview

You may license PRIORITY either under a non-commercial use license or under a specially-negotiated non-exclusive commercial use license. You may choose which type of license is more appropriate for your needs. For strictly non-commercial use of the software, you may prefer to license the software under the non-commercial use license. The term ‘commercial use’ is defined broadly: if the software is used for commercial gain or to further any commercial purpose, a commercial use license is required. If you have any question about whether your use would be considered commercial, or if you would like to negotiate a non-exclusive commercial use license, please contact us.