Aarhus University Seal

SPLICE-q: a Python tool for genome-wide quantification of splicing efficiency

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

  • Verônica R. de Melo Costa, Free University of Berlin, Max Planck Institute for Molecular Genetics
  • ,
  • Julianus Pfeuffer, Free University of Berlin, University of Tübingen
  • ,
  • Annita Louloupi, Helmholtz Association of German Research Centres
  • ,
  • Ulf A.V. Ørom
  • Rosario M. Piro, Polytechnic University of Milan

Background: Introns are generally removed from primary transcripts to form mature RNA molecules in a post-transcriptional process called splicing. An efficient splicing of primary transcripts is an essential step in gene expression and its misregulation is related to numerous human diseases. Thus, to better understand the dynamics of this process and the perturbations that might be caused by aberrant transcript processing it is important to quantify splicing efficiency. Results: Here, we introduce SPLICE-q, a fast and user-friendly Python tool for genome-wide SPLICing Efficiency quantification. It supports studies focusing on the implications of splicing efficiency in transcript processing dynamics. SPLICE-q uses aligned reads from strand-specific RNA-seq to quantify splicing efficiency for each intron individually and allows the user to select different levels of restrictiveness concerning the introns’ overlap with other genomic elements such as exons of other genes. We applied SPLICE-q to globally assess the dynamics of intron excision in yeast and human nascent RNA-seq. We also show its application using total RNA-seq from a patient-matched prostate cancer sample. Conclusions: Our analyses illustrate that SPLICE-q is suitable to detect a progressive increase of splicing efficiency throughout a time course of nascent RNA-seq and it might be useful when it comes to understanding cancer progression beyond mere gene expression levels. SPLICE-q is available at: https://github.com/vrmelo/SPLICE-q

Original languageEnglish
Article number368
JournalBMC Bioinformatics
Publication statusPublished - Dec 2021

Bibliographical note

Publisher Copyright:
© 2021, The Author(s).

    Research areas

  • Co-transcriptional splicing, RNA-seq, Splicing dynamics, Splicing efficiency

See relations at Aarhus University Citationformats

ID: 220611258