InPACT

InPACT identifies and quantifies intronic polyadenylation (IPA) events from conventional RNA sequencing (RNA-seq) data to characterize IPA-driven production of noncoding transcripts and truncated coding isoforms.

Key Features:

• IPA identification and quantification: Identifies and quantifies intronic polyadenylation (IPA) events using conventional RNA-seq data.
• Characterization of transcript consequences: Defines IPA-mediated generation of noncoding transcripts and transcripts with truncated coding regions.
• Discovery of unannotated IPA transcripts: Detects numerous previously unannotated IPA transcripts in human cells.
• Translation evidence integration: Integrates ribosome profiling data to corroborate translation of IPA-derived transcripts.
• Performance versus existing methods: Demonstrates superior identification and quantification of IPA events compared to existing methodologies.
• Dynamic context analysis: Reveals temporally coordinated IPA events during monocyte activation.
• Single-cell application: Applies to single-cell RNA-seq data from human fetal bone marrow to reveal context-specific IPA isoform expression.
• Alternative polyadenylation focus: Analyzes intronic alternative polyadenylation as a mechanism contributing to transcript diversity.
• Computational rigor: Employs rigorous computational analysis for precise identification and quantification of IPA events.
• Biological relevance: Links IPA and alternative polyadenylation events to biological processes and disease contexts.

Scientific Applications:

• Discovery of novel IPA transcripts: Identification of previously unannotated IPA transcripts in human cells.
• Assessing translation of IPA isoforms: Using ribosome profiling integration to determine which IPA-derived transcripts are translated.
• Studying immune activation dynamics: Characterizing temporally coordinated IPA during monocyte activation.
• Single-cell expression profiling: Mapping context-specific IPA isoforms in human fetal bone marrow single-cell RNA-seq data.
• Investigating disease mechanisms: Exploring the role of intronic polyadenylation and alternative polyadenylation in biological processes and diseases.

Methodology:

Computational analysis of conventional RNA-seq to identify and quantify IPA events with integration of ribosome profiling data for translation evidence.