FourCSeq

FourCSeq analyzes Circularized Chromosome Conformation Capture (4C) sequencing data to detect and quantify viewpoint-centric genomic interactions and their changes across biological conditions.

Key Features:

• Interaction detection: Identifies specific DNA interactions originating from a defined viewpoint by distinguishing interaction peaks from the background distance-dependent decay of ligation frequencies.
• Statistical modeling and peak calling: Models the overall trend with a smooth, monotonically decreasing function on transformed count data and computes residuals and z-scores to call significant interaction peaks.
• Comparative differential analysis: Normalizes fragment counts between samples and applies DESeq2 to detect differential contact frequencies across biological conditions or cell types.
• Preprocessing and alignment inputs: Provides a Python script for demultiplexing libraries and trimming primer sequences and accepts BAM files produced by standard alignment software as input.
• R package implementation: Implements the end-to-end analysis pipeline within an R package for programmatic analysis of 4C data.

Scientific Applications:

• Chromatin architecture mapping: Mapping spatial interactions between a viewpoint and genomic regions to study three-dimensional genome organization.
• Regulatory interaction identification: Identifying enhancer-promoter and other regulatory contacts linked to gene regulation.
• Comparative studies: Detecting condition-, cell type-, or disease-associated changes in chromosomal conformation.

Methodology:

Demultiplexing and primer trimming with a provided Python script; generation of BAM files by standard aligners; summarization of ligation frequencies as fragment counts; modeling distance-dependent decay with a smooth, monotonically decreasing function on transformed counts; calculation of residuals and z-scores to call peaks; normalization of fragment counts between samples and testing for differential contacts using DESeq2.