SCAPTURE

SCAPTURE identifies and quantifies cleavage and polyadenylation sites (PAS) from 3' tag-based single-cell RNA sequencing (scRNA-seq) data to enable analysis of alternative polyadenylation and PAS usage at single-cell resolution.

Key Features:

• Input data: Accepts 3' tag-based single-cell RNA sequencing (scRNA-seq) data for PAS analysis.
• Stepwise pipeline: Implements a stepwise methodological approach to identify, evaluate, and quantify cleavage and polyadenylation sites.
• De novo PAS detection: Detects PAS de novo in individual cells, enabling discovery of previously unannotated PAS.
• PAS quantification: Quantifies alternative PAS transcripts to measure PAS usage at the single-cell level.
• Deep learning evaluation: Embeds deep learning techniques within the pipeline to evaluate and increase precision of PAS detection.
• Single-cell resolution: Provides high-resolution, single-cell precision for observing changes in PAS usage.
• High sensitivity and accuracy: Designed for sensitive and accurate identification and evaluation of cleavage and polyadenylation sites.

Scientific Applications:

• Cell identity refinement: Refines cell identity analysis beyond traditional gene expression profiling by incorporating PAS usage information.
• Alternative polyadenylation studies: Enables investigation of alternative polyadenylation and post-transcriptional regulation through quantification of alternative PAS transcripts.
• Cellular heterogeneity: Supports dissection of cellular heterogeneity by revealing cell-type-specific PAS usage.
• Differential PAS analysis in PBMCs: Has been applied to peripheral blood mononuclear cells (PBMCs) from infected and healthy individuals to identify differential PAS usage.

Methodology:

Uses a stepwise computational pipeline to identify, evaluate, and quantify cleavage and polyadenylation sites from 3' tag-based scRNA-seq and embeds deep learning models for PAS detection and evaluation.