Ĭo-transcriptional splicing RNA-seq Splicing dynamics Splicing efficiency. It is almost similar to string splicing in python except that we splice lists. 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. The list splicing operation in Python is used to concatenate two or more lists. 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. Introns are generally removed from primary transcripts. Here, we introduce SPLICE-q, a fast and user-friendly Python tool for genome-wide SPLICing Efficiency quantification. SPLICE-q: a Python tool for genome-wide quantification of splicing efficiency. 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. are generally removed from primary transcripts to form mature RNA molecules in a post-transcriptional process called splicing.
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