BayesPI

BayesPI estimates transcription factor (TF) binding energetics and predicts protein–DNA interaction patterns by combining Bayesian model regularization with biophysical modeling for analysis of ChIP-chip and other high-throughput data.

Key Features:

• Bayesian Model Regularization: Employs Bayesian techniques to regularize models and refine estimation of TF binding energy matrices.
• Biophysical Modeling: Incorporates biophysical principles to compute TF binding affinity and chemical potential.
• Estimation of Binding Energy Matrices: Estimates binding energy matrices for transcription factors to quantify sequence-dependent binding preferences.
• High-Throughput Data Integration: Analyzes large-scale datasets such as synthetic and yeast ChIP-chip experiments to assess genome-wide nucleosome positioning and TF binding patterns.
• Adaptive Modifications Analysis: Detects adaptive modifications in TF binding parameters and nucleotide affinities underlying condition-specific and species-specific binding patterns.

Scientific Applications:

• Synthetic and Yeast ChIP-chip Analysis: Applied to synthetic ChIP-chip datasets and yeast ChIP-chip experiments to infer TF binding energetics.
• Genome-wide Nucleosome and TF Pattern Analysis: Characterizes genome-wide nucleosome positioning and TF binding patterns across conditions.
• Condition- and Species-specific Interaction Studies: Investigates condition-specific and species-specific changes in TF binding parameters and nucleotide affinities relevant to transcriptional regulation and evolutionary divergence.

Methodology:

Bayesian model regularization is applied to estimate transcription factor binding energy matrices.
Binding affinity and chemical potential are computed from the estimated energy matrices.
Analyses identify adaptive modifications in TF binding parameters and nucleotide affinities across datasets.