PRIORITY

PRIORITY enhances de novo motif discovery within transcription factor (TF) binding sites by incorporating informative priors over sequence positions to refine motif search and predict TF structural classes.

Key Features:

• Informative Priors: Incorporates prior probabilities that a TF binding site occurs at specific sequence positions to guide motif discovery.
• Class-Specific Priors: Employs classifiers trained on TRANSFAC to generate priors for three TF structural classes: basic leucine zipper, forkhead, and basic helix loop helix.
• Default Prior for Other Classes: Includes a default prior to accommodate TFs not covered by the specified classes.
• Integration into Motif Algorithm: Integrates informative and class-specific priors into the de novo motif-finding algorithm to improve motif detection.
• TF Structural Class Prediction: Predicts the structural class of TFs recognizing identified binding sites based on sequence-derived priors.

Scientific Applications:

• Motif Discovery in TF Binding Sites: Refines identification of de novo TF binding motifs within DNA sequences for studies of regulatory elements.
• TF Structural Classification: Supports prediction of TF structural classes from sequence data to aid interpretation of binding specificity.
• Analysis of ChIP-chip Data: Has been applied to yeast intergenic regions identified by ChIP-chip to identify motifs and predict TF classes.
• Gene Regulation Studies: Facilitates investigation of transcriptional regulatory mechanisms by linking motifs to TF classes and binding locations.

Methodology:

Priority incorporates informative priors over sequence positions, uses classifiers trained on TRANSFAC to produce class-specific priors for basic leucine zipper, forkhead, and basic helix loop helix classes, includes a default prior for other TFs, and integrates these priors into its de novo motif-finding algorithm.