crupR

crupR predicts condition-specific enhancers from ChIP-seq data through a four-step workflow for regulatory unit prediction.

Key Features:

• ChIP-seq count normalization: Normalizes ChIP-seq counts as the first step in the enhancer prediction workflow.
• Genome-wide active enhancer prediction: Predicts active enhancers binwise across the whole genome.
• Condition-specific clustering: Clusters putative active enhancers based on condition specificity.
• Target gene detection: Detects possible target genes of condition-specific clusters using RNA-seq counts.

Scientific Applications:

• Enhancer prediction: Identifies condition-specific enhancers from ChIP-seq data in a workflow-based framework.
• Regulatory unit analysis: Defines regulatory units by integrating ChIP-seq and RNA-seq data across multiple conditions.

Methodology:

The prediction of regulatory units follows four main steps: normalization of ChIP-seq counts, binwise prediction of active enhancers on the whole genome, condition-specific clustering of putative active enhancers, and detection of possible target genes using RNA-seq counts.