FiToM

FiToM identifies transcription factor binding sites in DNA and RNA sequences and estimates their binding affinities to support analysis of gene regulation and evolutionary dynamics.

Key Features:

• Detection and Affinity Calculation: Implements multiple techniques for detecting potential binding sites and estimating their binding affinities.
• Information Theory-Based Methods: Applies information theory–based methods to model binding site information content and search behavior.
• Relative Entropy Consideration: Explicitly uses and assesses Relative Entropy as a metric with attention to its assumptions.
• Complementary Genomic Cues: Integrates additional genomic features such as curvature to complement sequence information in site detection.
• Genomic Skew Considerations: Accounts for limitations of methods in skewed genomes and the impact of genomic skew on detection performance.
• Evolutionary Insights: Incorporates observations that binding sites evolve toward genomic skew and preserve information content through increased conservation.
• Revised Paradigm on Information Content: Treats information content as compound with respect to search efficiency and binding affinity and emphasizes unassuming search methods without heuristic corrections.

Scientific Applications:

• Genomic Analysis: Identifies transcription factor binding sites across bacterial genomes to inform studies of gene regulation and evolutionary patterns.
• Benchmarking and Validation: Benchmarks information theory–based approaches and addresses overestimation of search efficiency observed when using artificial data versus real genomic data.

Methodology:

Implements multiple detection techniques and binding-affinity estimation methods using information theory–based approaches including Relative Entropy, integrates genomic features such as curvature, evaluates effects of genomic skew, benchmarks against artificial data, and employs unassuming search methods without heuristic corrections.