PathwaySplice

PathwaySplice performs unbiased pathway analysis of alternative splicing events from RNA-Seq data by correcting for selection bias introduced by gene exon or junction counts to identify significant pathways and driver genes.

Key Features:

• Bias Adjustment for Exon/Junction Count: Adjusts pathway enrichment statistics to account for the number of exons or splice junctions per gene to reduce selection bias.
• Visualization and Bias Testing: Visualizes selection bias due to exon/junction count differences and applies logistic regression to formally test for such biases.
• Support for Diverse Gene Sets: Performs analyses using Gene Ontology terms, MSigDB collections, or user-defined gene sets.
• Identification of Significant Genes: Identifies genes that drive pathway significance from lists of alternative splicing events.
• Enrichment Map Organization: Organizes significant pathways into an enrichment map that groups pathways by overlapping genes.

Scientific Applications:

• Alternative splicing pathway discovery: Detects pathways enriched for alternative splicing events identified from RNA-Seq studies.
• Investigation of splicing regulatory mechanisms: Supports analyses aimed at elucidating regulatory mechanisms of alternative splicing.
• Functional interpretation in genomics and systems biology: Facilitates integration with Gene Ontology and MSigDB for functional interpretation in genomics, transcriptomics, and systems biology contexts.

Methodology:

Operates on alternative splicing events from RNA-Seq data with exon/junction-count bias adjustment, visualization of selection bias, logistic regression for bias testing, identification of significant genes driving pathway results, and organization of significant pathways via an enrichment map.