Year: 2023
Type: article
Abstract: Alternative polyadenylation (APA) is an important post-transcriptional mechanism that has major implications in biological processes and diseases. Although specialized sequencing methods for polyadenylation exist, availability of these data are limited compared to RNA-sequencing data. We developed REPAC, a framework for the analysis of APA from RNA-sequencing data. Using REPAC, we investigate the landscape of APA caused by activation of B cells. We also show that REPAC is faster than alternative methods by at least 7-fold and that it scales well to hundreds of samples. Overall, the REPAC method offers an accurate, easy, and convenient solution for the exploration of APA.
Source: Genome biology
Institutions: Weill Cornell Medicine, Johns Hopkins University
Language: English
Topic: RNA Research and Splicing
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,290
Funders: National Science Foundation, U.S. Department of Defense, Foundation for the National Institutes of Health, National Institutes of Health, Weill Cornell Medical College, National Cancer Institute, National Institute of General Medical Sciences, Congressionally Directed Medical Research Programs, DOD Prostate Cancer Research Program
Awards: G1153403171, G13210670, G1626146318, DEVELOPING A PTEN-ERG SIGNATURE TO IMPROVE MOLECULAR RISK STRATIFICATION IN PROSTATE CANCER, Hardwiring Mechanism into Predicting Cancer Phenotypes by Computational Learning, Developing a PTEN-ERG Signature to Improve Molecular Risk Stratification in Prostate Cancer, CIF21 DIBBs: Long Term Access to Large Scientific Data Sets: The SkyServer and Beyond, G4119534169, G4298445392, G4349108108, G5607769708, G5850682332, Hardening and Scaling Core Genomics Software, G761683009, G8176447971, G9000038747, G9000049842, G9000111784
OpenAlex ID: W4319731277