r3Cseq

r3Cseq analyzes 3C-seq (chromosome conformation capture coupled with next-generation sequencing) data to identify statistically significant long-range chromosomal interactions and support interpretation of regulatory elements such as promoters and enhancers.

Key Features:

• Implementation: Provided as an R/Bioconductor package for analysis of 3C-seq datasets.
• Data input: Reads common aligned read input formats for downstream processing of 3C-seq data.
• Normalization: Implements robust data normalization techniques specific to 3C-seq experiments.
• Viewpoint-centered analysis: Supports analysis of interactions between a chosen genomic viewpoint and other regions across the genome.
• Visualization: Generates visualizations of candidate interaction regions within the genomic context.
• Statistical analysis: Detects statistically significant chromatin interactions from 3C-seq data.

Scientific Applications:

• Genome architecture mapping: Characterizes the spatial organization of the genome using 3C-seq interaction data.
• Regulatory element interaction analysis: Identifies interactions involving promoters, enhancers, and other regulatory elements.
• Viewpoint-specific studies: Maps genome-wide contacts of a selected genomic viewpoint to study regulatory relationships.
• Interpretation of experimental results: Supports generation and interpretation of hypotheses about long-range regulatory mechanisms affecting gene expression.

Methodology:

Processes aligned 3C-seq reads through normalization and visualization steps and applies statistical methods to identify significant chromatin interactions.