Piranha

Piranha calls peaks in high-throughput protein–RNA interaction datasets from CLIP-Seq and RIP-Seq to identify statistically significant RNA-binding protein binding sites and support analysis of post-transcriptional and co-transcriptional regulation.

Key Features:

• Versatility Across Technologies: Applies to multiple CLIP and RIP-Seq protocol variations to support different experimental setups.
• Accurate Modeling of Read-Count Distributions: Employs statistical models to represent read-count distributions and assess the statistical significance of peaks.
• Incorporation of External Covariates: Integrates external covariates such as transcript abundance into peak calling to account for correlations with read counts.
• Comparative Analysis Across Conditions: Facilitates direct comparisons of site usage across cell types or experimental conditions to detect differential binding occupancy.
• Input Formats: Accepts BED and BAM input formats for compatibility with standard genomic datasets.

Scientific Applications:

• RBP Binding Site Identification: Detects statistically significant RNA-binding protein binding sites from CLIP-Seq and RIP-Seq data.
• Post-transcriptional and Co-transcriptional Regulation Analysis: Enables study of regulatory mechanisms mediated by RNA-binding proteins linking genotype to phenotype.
• Differential Binding Analysis: Supports comparison of binding site usage across cell types or conditions to study dynamic changes in protein–RNA interactions.
• Refinement Using Transcript Abundance: Uses transcript abundance as a covariate to refine detection of genuine binding sites versus background signal.

Methodology:

Models read-count distributions using statistical methods, incorporates external covariates such as transcript abundance into the peak-calling model, and accepts BED and BAM as input.