SpliceTrap

SpliceTrap quantifies exon inclusion ratios from paired-end RNA-seq data to estimate exon-level expression for studying alternative splicing regulation.

Key Features:

• Bayesian inference approach: Employs a Bayesian inference framework to estimate exon inclusion levels.
• Exon-centric estimation: Treats each exon as an independent entity to provide exon-level expression rather than full-length transcript isoform estimates.
• AS event detection without background reads: Identifies major classes of alternative splicing events under a single cellular condition without requiring a background set of reads.
• Characterization of splicing variations: Quantifies exon inclusion, skipping, and size variations due to alternative 3’/5’ splice sites and intron retention.
• Benchmarking on simulated and real data: Demonstrated accuracy and robustness through testing with simulated and real RNA-seq data compared to state-of-the-art transcript quantification tools.

Scientific Applications:

• Transcriptomic mapping: Generating accurate exon-level transcriptomic maps from paired-end RNA-seq data.
• Alternative splicing regulation studies: Investigating regulation of alternative splicing, a process affecting over 90% of human genes.
• Functional inference: Exploring how alternative splicing contributes to protein diversity, gene expression variability, and cellular function.

Methodology:

Uses paired-end RNA-seq input and a Bayesian inference framework that treats each exon independently to estimate inclusion levels and to detect exon inclusion, skipping, and size variations (alternative 3’/5’ splice sites and intron retention) and major classes of alternative splicing events without a background read set.