IPScan: Detecting novel intronic PolyAdenylation events with RNA-seq data.

Area of Science:

• Molecular Biology
• Bioinformatics
• Genomics

Background:

• Intronic PolyAdenylation (IPA) is a post-transcriptional mechanism that increases transcriptome and proteome diversity by truncating transcript coding regions.
• This process generates novel protein isoforms, some linked to cancer progression, potentially by losing tumor-suppressive functions and contributing to oncogenesis.
• Existing RNA-seq methods face challenges in accurately detecting and quantifying novel IPA events.

Purpose of the Study:

• To develop a computational pipeline, IPScan, for precise identification, quantification, and visualization of Intronic PolyAdenylation events.
• To address the limitations of current methods in detecting and quantifying novel IPA events.

Main Methods:

• Development of the IPScan computational pipeline for IPA event analysis.
• Benchmarking IPScan against existing methods using simulated data, human and mouse cell lines.
• Analysis of The Cancer Genome Atlas (TCGA) breast cancer datasets.
• Quantification and validation of differential IPA events using qPCR.

Main Results:

• IPScan enables precise identification, quantification, and visualization of IPA events.
• The pipeline demonstrated robust performance when benchmarked against existing methods and real-world datasets, including TCGA breast cancer data.
• Differential IPA events were successfully quantified under various biological conditions and validated experimentally.

Conclusions:

• IPScan is an effective computational tool for the accurate detection and quantification of Intronic PolyAdenylation events.
• This tool can advance the understanding of IPA's role in biological processes, particularly in cancer.
• The findings highlight the importance of IPA in generating protein diversity and its implications in oncogenesis.