Login / Signup

REPAC: analysis of alternative polyadenylation from RNA-sequencing data.

Eddie Luidy ImadaChristopher WilksBen LangmeadLuigi Marchionni
Published in: Genome biology (2023)
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.
Keyphrases
  • single cell
  • electronic health record
  • big data
  • gene expression
  • machine learning
  • transcription factor
  • oxidative stress
  • data analysis
  • artificial intelligence
  • mass spectrometry
  • deep learning