Jutils

Jutils visualizes differential alternative splicing events to integrate outputs from multiple detection methods and support analysis of splicing patterns relevant to development, tissue specialization, and disease.

Key Features:

• Method-agnostic compatibility: Converts outputs from differential splicing detection tools including MntJULiP, LeafCutter, MAJIQ, and rMATS into a unified representation.
• Unified data representation: Transforms diverse DS tool outputs into a standardized data format for consistent downstream visualization.
• Heatmaps: Generates heatmaps of splice ratios using both absolute values and Z-score normalization.
• Sashimi plots: Produces sashimi plots at the intron (splice junction) level to visualize individual splicing events.
• Venn diagrams: Creates Venn diagrams to compare results from multiple analyses and identify shared and unique splicing events.
• Lightweight execution: Operates from a single unified data file to generate visualizations.
• Implementation: Implemented in Python.

Scientific Applications:

• Alternative splicing analysis: Provides visual summaries to analyze gene-level and event-level alternative splicing.
• Comparative method and dataset analysis: Enables comparison of splicing results across detection methods and conditions to identify concordant and method-specific events.
• Biological interpretation: Supports identification of splicing patterns associated with development, tissue specialization, and disease to link splicing changes to phenotypic differentiation.

Methodology:

Converts outputs from DS tools (MntJULiP, LeafCutter, MAJIQ, rMATS) into a standardized unified data file, applies Z-score normalization for heatmaps, and generates heatmaps, sashimi plots at the intron/splice-junction level, and Venn diagrams; implemented in Python.