iASeq

iASeq detects allele-specific protein–DNA binding (ASB) events by employing a Bayesian hierarchical mixture model to jointly analyze multiple ChIP-seq datasets and infer correlation patterns of allele-specificity across proteins.

Key Features:

• Joint analysis of multiple datasets: Integrates multiple ChIP-seq studies to increase statistical power and accuracy in detecting allelic imbalances compared to single-dataset analyses.
• Bayesian hierarchical mixture model: Uses a Bayesian hierarchical mixture model to learn correlation patterns among allele-specificities across different proteins.
• Information borrowing across datasets: Identifies correlations between datasets and borrows information to enhance detection sensitivity and reliability of ASB events.
• Application in large-scale studies: Demonstrated on 77 ChIP-seq samples from 40 ENCODE datasets and a genomic DNA sample in GM12878 cells, showing correlated allele-specificities among multiple proteins.

Scientific Applications:

• Allele-specific protein–DNA interaction studies: Improves detection and characterization of ASB events across multiple proteins and conditions.
• Genetic basis of phenotypes and disease: Supports analyses that link allele-specific binding patterns to genetic effects underlying biological processes and disease mechanisms.

Methodology:

iASeq applies a Bayesian hierarchical mixture model to model and learn correlation patterns of allele-specificity across proteins and borrows information across multiple ChIP-seq datasets to improve inference of ASB events.