LeafCutterMD

LeafCutterMD extends LeafCutter to detect outlier splicing events from next-generation RNA-seq data without predefined gene annotations, enabling identification of aberrant splicing underlying rare Mendelian diseases.

Key Features:

• Annotation-Free Quantification: Quantifies RNA splicing events directly from next-generation sequencing (RNA-seq) data without relying on predefined gene annotations.
• Outlier Splicing Detection: Identifies outlier splicing events indicative of aberrant splicing that may reveal disease-associated genes in patient samples.
• Improved Statistical Framework: Implements a refined statistical framework that increases power to detect splicing outliers while controlling false-positive rates.
• Simulation and Validation: Uses rigorous simulations and validation against real patient data to assess performance and false-positive control.
• Application in Rare Disease Cohorts: Applied to a cohort of disease-affected probands from the Mayo Clinic Center for Individualized Medicine, recovering all aberrantly spliced genes previously confirmed by manual curation.

Scientific Applications:

• Rare Mendelian Disease Gene Discovery: Detects aberrant splicing events to support identification of causal genes that may be missed by DNA sequencing alone.
• Clinical Cohort Analysis: Analyzes patient RNA-seq cohorts (e.g., Mayo Clinic Center for Individualized Medicine probands) to pinpoint aberrantly spliced genes for clinical research.

Methodology:

Annotation-free quantification of splicing from next-generation RNA-seq, a refined statistical framework for outlier detection, and validation via rigorous simulations and testing on real patient cohorts (Mayo Clinic Center for Individualized Medicine).