scQuint

scQuint quantifies intronic usage from short-read single-cell RNA sequencing (scRNA-seq) to detect and characterize alternative splicing and novel isoforms without relying on existing gene annotations.

Key Features:

• Annotation-Free Analysis: Operates independently of existing gene annotations to detect unannotated splice junctions and isoforms.
• Intronic Quantification: Quantifies intronic sequences from short-read scRNA-seq to measure isoform variation.
• Robust Isoform Variation Detection: Detects alternative splicing events and isoform differences across cells using short-read data.
• Comprehensive Dataset Analysis: Applied to large-scale single-cell datasets including Tabula Muris and the BRAIN Initiative Cell Census Network (BICCN).
• Cell-Type-Specific Insights: Reveals strong cell-type-specific alternative splicing patterns in examples such as primary motor cortex neurons, bone marrow B cells, and epithelial cells.
• Predictive Modeling for Splicing Regulation: Builds predictive models based on splicing factor activity to recover known regulatory interactions and propose new hypotheses for genes such as Khdrbs3 and Rbfox1.

Scientific Applications:

• Alternative Splicing Analysis: Characterizes cell-type-resolved alternative splicing landscapes from short-read scRNA-seq data.
• Novel Isoform Identification: Identifies previously unannotated isoforms and splicing events that are not captured by total gene expression differences.
• Regulatory Network Exploration: Infers splicing factor–isoform relationships and generates hypotheses about splicing regulation.

Methodology:

Annotation-free analysis that quantifies intronic sequences from short-read scRNA-seq, integrates large-scale single-cell datasets (e.g., Tabula Muris, BICCN), addresses technical artifacts and unannotated isoforms, and uses predictive models based on splicing factor activity to recover regulatory interactions.