BoBro

BoBro predicts and analyzes cis-regulatory motifs in genomic sequences to identify statistically significant motifs, their instances, co-occurring motifs, and motif clusters for studying gene regulatory mechanisms.

Key Features:

• Genome-scale motif identification: Identifies statistically significant cis-regulatory motifs across entire genomes.
• Accurate motif instance scanning: Employs a novel statistical method for P-value estimation to scan and identify instances of query motifs within specified genomic regions.
• Motif comparison and clustering: Compares and clusters identified motifs while considering weak signals from motif flanking regions to improve grouping accuracy.
• Co-occurring motif analysis: Detects and analyzes co-occurring motifs within regulatory regions to support inference of potential joint transcription factor regulation.

Scientific Applications:

• Comparative benchmarking with MEME: Demonstrated, on Escherichia coli K12 and human genomes, improved performance relative to MEME in genome-scale motif identification, motif instance accuracy, and motif clustering reliability.

Methodology:

Implements novel/advanced statistical techniques for P-value estimation and incorporates contextual weak signals from flanking regions to improve motif detection, instance scanning, comparison, and clustering.