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

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Melo Costa,  Verônica R.
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Melo Costa2020_Preprint.pdf
(Preprint), 548KB

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Citation

Melo Costa, V. R., Pfeuffer, J., Louloupi, A., Ørom, U. A. V., & Piro, R. M. (2020). SPLICE-q: aPython tool for genome-wide quantification of splicing efficiency. BioRxiv (PrePrint Server). doi:10.1101/2020.10.12.318808.


Cite as: http://hdl.handle.net/21.11116/0000-0007-827E-9
Abstract
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.