FRASER

FRASER detects aberrant splicing events in RNA-seq data to identify splicing anomalies, including alternative splicing and intron retention, that contribute to rare genetic diseases.

Key Features:

• Comprehensive detection: Identifies both alternative splicing and intron retention from RNA-seq, effectively doubling the number of aberrant splicing events detected.
• Pathogenic event identification: Has identified pathogenic intron retention events such as in the MCOLN1 gene.
• Confounding factor control: Automatically controls for latent confounders to improve sensitivity and reliability of splicing anomaly detection.
• Statistical rigor: Applies a count distribution model with multiple testing correction, reducing false positives by two orders of magnitude compared to z-score cutoffs while minimizing sensitivity loss.
• Methodological foundations: Incorporates adaptations from the Leafcutter method (Kremer et al.) and is implemented with an R package (FRASER) and a Python package (wBuild).

Scientific Applications:

• Rare disease diagnostics: Uses RNA-seq to detect splicing defects that can explain genetic causes of rare diseases.
• Reprioritization of pathogenic variants: Aids in reprioritizing pathogenic aberrant exon truncations, exemplified by analysis of the TAZ gene.
• Detection of pathogenic intron retention: Enables discovery of disease-associated intron retention events such as those observed in MCOLN1.

Methodology:

Uses a count distribution model with multiple testing correction, automatic control for latent confounders, and adaptations of the Leafcutter method.