GRISOTTO

GRISOTTO uncovers regulatory motifs in DNA sequences by post-processing outputs of combinatorial algorithms using prior information derived from sets of co-regulated sequences.

Key Features:

• Prior Information Integration: Post-processes outputs of combinatorial algorithms with prior knowledge from co-regulated DNA sequences to refine motif searches.
• Combining Multiple Priors: Integrates priors from multiple sources to improve motif discovery performance compared to single-prior use.
• Improved Accuracy and Efficiency: Incorporation of priors in post-processing enhances the accuracy and efficiency of motif identification.

Scientific Applications:

• Motif discovery in DNA regulatory regions: Identification of regulatory motifs in DNA sequences using priors derived from co-regulated sequence sets.
• Evaluation of prior integration strategies: Comparison of combined versus individual priors to assess their impact on motif identification performance.

Methodology:

Post-processes outputs of combinatorial motif-discovery algorithms using prior information derived from co-regulated sequences and integrates priors from multiple sources.