motif_prob

motif_prob computes exact motif count distributions in biological sequences to quantify motif occurrences and provide exact p-values for motif enrichment and deviation analyses.

Key Features:

• Exact count distribution: Implements an exact formula for motif count distributions via progressive approximation with arbitrary precision.
• Error-bound iterative process: Uses an efficient error-bound iterative process to accelerate computations while preserving numerical accuracy.
• Implementation: Provided implementations in Perl and C++ for computational execution.
• Statistical robustness: Avoids reliance on Markovian assumptions and improves on compound Poisson approximations for reliable p-value computation.
• Performance: Demonstrates reported runtime improvements over MoSDi (50–1000× faster for exact calculations and 60–120× faster versus compound Poisson approximation in benchmarks).
• Scalability: Reported to process datasets such as one million motifs of 13–31 bases over genomes up to 5 million bases within minutes on standard hardware.
• Validation: Validated against MoSDi for precision and efficiency in benchmark comparisons.
• Enrichment quantification: Produces exact p-values for motif enrichment and deviations from expected frequency ranges.

Scientific Applications:

• Genetic and evolutionary studies: Quantify motif occurrences for genetic research, gene evolution studies, and analyses of evolutionary patterns.
• Transcription site analysis: Assess motif distributions and significance at transcription sites.
• Disease-associated motif analysis: Investigate motif associations relevant to complex genetic diseases.
• Bacterial motif characterization: Characterize bacterial motifs and assess enrichment of motifs associated with antimicrobial resistance genes across genomes with varying GC content.
• Enrichment testing: Provide exact p-value-based testing for motif enrichment and deviation analyses across diverse datasets.

Methodology:

Implements an exact formula for motif count distributions via progressive approximation with arbitrary precision using an efficient error-bound iterative process, and is implemented in Perl and C++.