![]() Yet, this methodology can only investigate a limited number of genes. The efficiency of splicing is commonly quantified by means of RT-qPCR with primers that span exon-exon and exon–intron boundaries. Thus, to better understand the dynamics of splicing and the perturbations that might be caused by aberrant transcript processing, it is important to quantify splicing efficiency. Up to 15% of mutations that cause genetic disease have been suggested to affect pre-mRNA splicing. Splicing is an essential step in gene expression and its misregulation is related to numerous human diseases. melanogaster are co-transcriptionally spliced, as well as in mouse and many human cells and tissues. cerevisiae, data revealed polymerase pausing within the terminal exon, permitting enough time for splicing to happen before release of the mature RNA and nascent RNA also indicated that most introns in D. More recently, genome-wide studies in different cell lines and organisms using nascent RNA showed introns being spliced shortly after their transcription is finished: in S. melanogaster chorion genes using electron microscopy to observe the assembly of spliceosomes at the splice junctions in nascent transcripts. Co-transcriptional splicing was first suggested in D. Splicing is dynamic and occurs mostly during or immediately after the transcription of a complete intron. These will act as spliceosome regulators as well as splice site choice modulators and are particularly important for efficient transcript processing. In metazoans, further sequences are required for recruiting different trans-acting regulatory factors. These events promote the correct folding necessary for the intron’s excision and are followed by the correct pairing of the exon-exon boundaries. The exon–intron boundaries, i.e., the splice junctions, together with the branch point, a short sequence located 18–40 nucleotides upstream of the intron's 3' splice junction and the polypyrimidine tract, are recognized by the spliceosome. This highly regulated process consists basically of a series of hydrolysis and ligation reactions led by the spliceosome. These sequences are generally removed from primary transcripts by a post-transcriptional process called splicing to form mature RNA molecules. SPLICE-q is available at: Įukaryotic genes are mostly composed of a series of exons intercalated by sequences with no coding potential called introns. ![]() 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. We also show its application using total RNA-seq from a patient-matched prostate cancer sample. We applied SPLICE-q to globally assess the dynamics of intron excision in yeast and human nascent RNA-seq. 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. It supports studies focusing on the implications of splicing efficiency in transcript processing dynamics. Here, we introduce SPLICE-q, a fast and user-friendly Python tool for genome-wide SPLICing Efficiency quantification. 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. ![]() An efficient splicing of primary transcripts is an essential step in gene expression and its misregulation is related to numerous human diseases. Introns are generally removed from primary transcripts to form mature RNA molecules in a post-transcriptional process called splicing.
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