MotifSearch

MotifSearch identifies transcription factor binding sites in DNA sequences to map regulatory motifs and support analysis of gene regulation.

Key Features:

• Criteria Specification: Allows specification of statistical criteria including p-values and approximate false-positive rates estimated from negative examples.
• Consensus Sequences: Searches for potential binding sites using known consensus sequences of transcription factors.
• Position-Specific Scoring Matrices (PSSMs): Scores and ranks genomic regions by similarity to known binding motifs using PSSMs.
• Average Nucleotide Matches: Computes the average number of nucleotide matches between a candidate site and known sites as a basic similarity measure.
• Per-Position Information Content: Incorporates information content at each motif position to account for positional variability in nucleotide frequency.
• Local Pairwise Nucleotide Dependencies: Models local pairwise correlations between nucleotides within binding sites to capture dependencies beyond single-position models.

Scientific Applications:

• Regulatory network mapping: Identification of transcription factor binding sites to map regulatory interactions within genomes.
• Gene regulation inference: Inference of gene regulation mechanisms by locating TF–DNA interactions that influence transcription.
• Expression pattern prediction: Prediction of gene expression patterns through identification of regulatory motifs associated with transcriptional control.
• Evolutionary conservation analysis: Exploration of evolutionary conservation of regulatory elements by comparing identified motifs across sequences.

Methodology:

MotifSearch applies consensus-sequence matching, position-specific scoring matrices (PSSMs), and average nucleotide-match calculations; integrates per-position information content and local pairwise nucleotide dependencies; computes p-values or approximate false-positive rates from negative examples; and was validated by cross-validation on a dataset of Escherichia coli transcription factors and their binding sites, demonstrating improved identification when incorporating per-position information content and local pairwise dependencies.