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

Intronic PolyAdenylation (IPA) is an important post-transcriptional mechanism that can alter transcript coding potential by truncating translation regions, thereby increasing transcriptome and proteome diversity. This process generates novel protein isoforms with altered peptide sequences, some of which are implicated in disease progression, including cancer. Truncated proteins may lose tumor-suppressive functions, contributing to oncogenesis. Despite advancements in Alternative PolyAdenylation (APA) analysis using RNA-seq, detecting and quantifying novel IPA events remains challenging. To address this, we developed IPScan, a computational pipeline for precise IPA event identification, quantification, and visualization. IPScan has been benchmarked against existing methods using simulated data, different human and mouse cell lines, and TCGA (The Cancer Genome Atlas) breast cancer datasets. Differential IPA events under different biological conditions were quantified and validated via qPCR.

Authors

Institutions

Publication Details

Journal
PLoS Computational Biology
Published
2025-11-11
DOI
https://doi.org/10.1371/journal.pcbi.1013668
Citations
2
Primary Topic
RNA Research and Splicing
Type
article
Field-Weighted Citation Impact
1.61
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Sze Cheng, Jeongsik Yong, Naima Ahmed Fahmi, Wei Zhang et al.
2 citations
PLoS Computational Biology
RNA Research and Splicing
1.61
article

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

Sze Cheng, Jeongsik Yong, Naima Ahmed Fahmi, Wei Zhang, Qianqian Song, James W. Overstreet
article en
2 citations

Abstract

Intronic PolyAdenylation (IPA) is an important post-transcriptional mechanism that can alter transcript coding potential by truncating translation regions, thereby increasing transcriptome and proteome diversity. This process generates novel protein isoforms with altered peptide sequences, some of which are implicated in disease progression, including cancer. Truncated proteins may lose tumor-suppressive functions, contributing to oncogenesis. Despite advancements in Alternative PolyAdenylation (APA) analysis using RNA-seq, detecting and quantifying novel IPA events remains challenging. To address this, we developed IPScan, a computational pipeline for precise IPA event identification, quantification, and visualization. IPScan has been benchmarked against existing methods using simulated data, different human and mouse cell lines, and TCGA (The Cancer Genome Atlas) breast cancer datasets. Differential IPA events under different biological conditions were quantified and validated via qPCR.

PLoS Computational BiologyVol. 21(11)
University of Central Florida (US), University of Minnesota (US), University of Florida Health (US)
Openalex Percentile: Top 14%
RNA Research and Splicing
1.61
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

IPScan: Detecting novel intronic PolyAdenylation events with RNA-seq data — Sze Cheng, Jeongsik Yong, et al. · PLoS Computational Biology (2025) | TGRS Research Map | TGRS