CHiCAG

CHiCAG detects chromosomal interactions from Capture Hi-C (CHi-C) data to identify promoter-associated contacts and support interpretation of regulatory features and disease-associated single nucleotide polymorphisms (SNPs).

Key Features:

• Capture Hi-C (CHi-C) support: Processes targeted Capture Hi-C (CHi-C) datasets that profile interactions for regions of interest such as gene promoters.
• Specialized background model: Implements a specialized background model tailored to the statistical properties of CHi-C data.
• Normalization algorithms: Provides algorithms tailored for normalization of CHi-C experiments to mitigate technical biases.
• Multiple testing correction: Applies multiple testing procedures specifically adapted for the targeted nature of CHi-C data.
• Bias mitigation: Mitigates potential biases inherent in CHi-C data processing to improve accuracy of interaction calls.
• Promoter-focused detection: Detects promoter-interacting regions and other targeted genomic contacts.
• Biological validation: Reported promoter-interacting regions show enrichment for regulatory features and disease-associated SNPs.

Scientific Applications:

• Promoter interaction mapping: Identification of promoter-associated chromosomal interactions from Capture Hi-C data.
• Regulatory genomics: Characterization of regulatory elements intersecting promoter contacts and enrichment analyses for regulatory features.
• Disease variant interpretation: Linking promoter-interacting regions to disease-associated SNPs to aid interpretation of genetic associations.
• Chromosomal architecture studies: Analysis of DNA looping interactions and local chromosomal architecture using targeted Capture Hi-C.
• Comparison with Hi-C-like techniques: Addresses statistical challenges specific to targeted CHi-C data relative to other Hi-C-like approaches.

Methodology:

Incorporates a specialized background model and algorithms for normalization and multiple testing specifically adapted for CHi-C experiments.