@article{Rashmi13042026,
author = {Rashmi, Richa and Muruganandham, Abhinaya and Borkar, Pranita and Mallick, Sumana and Hubbs, Taylor and Aviles, Ari and Chung, Daniel and Singh, Irtisha}, 
title = {Dynamics of intronic polyadenylation in the hematopoietic lineage and its regulation by DNA methylation},
year = {2026}, 
doi = {10.1101/gr.281044.125}, 
elocation-id = {gr.281044.125}, 
abstract ={Intronic polyadenylation (IPA) is a key mechanism driving transcriptome diversity, yet its detection and functional characterization remain challenging due to complex splicing patterns and complexity of intronic regions. Here, we introduce IPAseek, a dynamic programming-based computational framework that leverages the Pruned Exact Linear Time (PELT) algorithm and Changepoints Over a Range of PenaltieS (CROPS) to enable de novo identification of IPA events from bulk RNA-seq data. IPAseek robustly detects both composite and skipped IPA isoforms. Applying IPAseek to bulk RNA-seq of hematopoietic cell types, reveals lineage and stage-specific IPA signatures, with lymphoid cells exhibiting higher IPA site usage than myeloid cells. Temporal profiling during megakaryocyte differentiation uncovers dynamic, gene-specific IPA regulation linked to functional pathways including peroxisomal metabolism and autophagy which are known to play a crucial role in megakaryocytic differentiation, impacting the development and maturation of megakaryocytes. Further, integrative analysis demonstrates that IPA site usage is associated with lower DNA methylation within introns, supporting a regulatory axis connecting epigenetic state and IPA. This finding aligns with emerging evidence that DNA methylation modulates alternative polyadenylation via CTCF-mediated chromatin looping. Thus, IPAseek provides a platform to characterize IPA across physiological systems and disease contexts using widely available bulk RNA-seq data. These IPA events can be further integrated with other regulatory datasets to elucidate their interplay and functional significance.}, 
URL = {http://genome.cshlp.org/content/early/2026/04/13/gr.281044.125.abstract}, 
eprint = {http://genome.cshlp.org/content/early/2026/04/13/gr.281044.125.full.pdf+html}, 
journal = {Genome Research} 
}