HiCNorm

HiCNorm normalizes Hi-C contact maps by removing systematic biases to produce corrected chromatin interaction matrices for downstream genomic analyses.

Key Features:

• Parametric model: Employs a parametric framework to relate chromatin interaction counts to systematic biases.
• Simplicity and speed: Uses fewer parameters than the Yaffe and Tanay model and is reported to run over 1000 times faster on large datasets.
• Reproducibility: Produces more reproducible normalized Hi-C data in real datasets, yielding more consistent downstream results.

Scientific Applications:

• Gene regulation: Provides bias-corrected contact maps to support analyses of chromatin interactions relevant to gene regulation.
• 3D genome organization: Enables investigation of three-dimensional genome organization from Hi-C data.
• Identification of functional genomic elements: Improves detection of interaction patterns used to identify functional genomic elements.
• High-throughput analyses: Scales to large Hi-C datasets to support high-throughput genomic studies.

Methodology:

Models the relationship between chromatin interactions and systematic biases at a chosen resolution using a parametric framework to adjust contact counts and produce a normalized contact map.