2021
DOI: 10.1186/s12859-021-04282-6
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SPLICE-q: a Python tool for genome-wide quantification of splicing efficiency

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. … Show more

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Cited by 14 publications
(9 citation statements)
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“…Splicing efficiency was quantified using SPLICE-q with default parameters(de Melo Costa et al 2021).…”
Section: Methodsmentioning
confidence: 99%
“…Splicing efficiency was quantified using SPLICE-q with default parameters(de Melo Costa et al 2021).…”
Section: Methodsmentioning
confidence: 99%
“…To quantify splicing efficiency of introns, we used SPLICE-q 81 version 1.0.0 to calculate the reverse intron expression ratio (IER) in protein coding genes using the Gencode v34 chromosomal annotation. By default, SPLICE-q uses the highest filtering settings for IER quantification, which selects only introns that do not overlap any exons of the same gene or of any other gene.…”
Section: Methodsmentioning
confidence: 99%
“…The correlation between biological duplicates for all RNA-seq couples according to Pearson coefficient was > 0.98. SPLICE-q (de Melo Costa et al 2021) was used for splicing efficiency quantification.…”
Section: Methodsmentioning
confidence: 99%