Chicago

Chicago analyzes Capture Hi-C (CHi-C) data to detect statistically significant chromosomal interactions involving targeted regions such as gene promoters.

Key Features:

• Background model and normalization: Implements a CHi-C-specific background model and normalization algorithms to adjust raw interaction frequencies and account for experimental and sequencing biases.
• Multiple testing procedures: Applies robust multiple testing correction methods to control false discovery rates in high-throughput interaction detection.
• Robust interaction detection: Provides robust detection of chromosomal interactions, including promoter-interacting regions, with high precision for Capture Hi-C data.

Scientific Applications:

• Regulatory feature enrichment: Identifies promoter-interacting regions that are enriched for regulatory features to support studies of gene regulation.
• Disease association studies: Links chromosomal interactions to disease-associated single nucleotide polymorphisms (SNPs) to facilitate investigation of regulatory mechanisms underlying genetic associations.
• Genomic research areas: Supports analyses in epigenomics, regulatory genomics, and genetic epidemiology by enabling analysis of Capture Hi-C datasets.

Methodology:

Chicago applies a CHi-C-specific background model, normalizes interaction frequencies to mitigate biases, uses statistical models and algorithms adapted for CHi-C data, and performs multiple testing corrections.