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

• Main Authors: de Melo Costa, V.R (Author), Louloupi, A. (Author), Ørom, U.A.V (Author), Pfeuffer, J. (Author), Piro, R.M (Author)
• Format: Article
• Language: English
• Published: BioMed Central Ltd 2021
• Subjects: Aberrant transcript; alternative RNA splicing; Alternative Splicing; Cancer progression; Co-transcriptional splicing; Diseases; Dynamics; Efficiency; Gene expression; Gene expression levels; genetics; genome; Genome; High level languages; human; Humans; intron; Introns; ITS applications; Post-transcriptional process; Prostate cancers; RNA; RNA molecules; RNA splice site; RNA Splice Sites; RNA splicing; RNA Splicing; RNA-seq; Splicing dynamics; Splicing efficiency; User friendly
• Online Access: View Fulltext in Publisher

 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 © 2021, The Author(s).