![]() We can freely use positive or negative indexing for assignment. But it’s also possible to change cell content using an assignment operation: > basket = > colors = īefore we used indexing only for accessing the content of a list cell. In negative indexing system -1 corresponds to the last element of the list(value ‘black’), -2 to the penultimate (value ‘white’), and so on. So, instead of using indexes from zero and above, we can use indexes from -1 and below. To address this requirement there is negative indexing. But what if we want to take the last element of a list? Or the penultimate element? In this case, we want to enumerate elements from the tail of a list. This is handy if we use position from the head of a list. Using indexing we can easily get any element by its position. To access an element by its index we need to use square brackets: > colors = That means, the first element(value ‘red’) has an index 0, the second(value ‘green’) has index 1, and so on. Each item in the list has a value(color name) and an index(its position in the list). It allows you to store an enumerated set of items in one place and access an item by its position – index. In Python, list is akin to arrays in other scripting languages(Ruby, JavaScript, PHP). ![]() Ĭo-transcriptional splicing RNA-seq Splicing dynamics Splicing efficiency.Before discussing slice notation, we need to have a good grasp of indexing for sequential types. 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. are generally removed from primary transcripts to form mature RNA molecules in a post-transcriptional process called splicing. 8 Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.7 Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark.6 Max Delbrück Center for Molecular Medicine, Berlin Institute for Medical Systems Biology, Berlin, Germany.5 Institute for Bioinformatics and Medical Informatics Tübingen, Eberhard Karls Universität Tübingen, Tübingen, Germany.4 Department of Computer Science, Eberhard Karls Universität Tübingen, Tübingen, Germany.3 Institute of Computer Science and Institute of Bioinformatics, Freie Universität Berlin, Berlin, Germany. ![]() 2 Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany. 1 Institute of Computer Science and Institute of Bioinformatics, Freie Universität Berlin, Berlin, Germany. ![]()
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