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RNA Splicing

RNA Splicing

Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...

Pre-mRNA Processing: RNA Splicing

Pre-mRNA Processing: RNA Splicing

Comparing Copy Number Variations and SNPs

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

Chromatin Structure and RNA Splicing

Chromatin Structure and RNA Splicing

Alternative RNA Splicing

Alternative RNA Splicing

Alternative RNA Splicing

Alternative RNA Splicing

Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
There are five types of alternative RNA splicing that vary in the ways the pre-mRNA segments are removed or retained in the mature mRNA. The first...

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Related Experiment Video

Updated: Oct 28, 2025

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Published on: December 9, 2016

SPLICE-q: a Python tool for genome-wide quantification of splicing efficiency.

Verônica R de Melo Costa^1,2, Julianus Pfeuffer^3,4,5, Annita Louloupi⁶

¹Institute of Computer Science and Institute of Bioinformatics, Freie Universität Berlin, Berlin, Germany. veronica.melocosta@gmail.com.

BMC Bioinformatics

|July 16, 2021

Summary

This summary is machine-generated.

We developed SPLICE-q, a Python tool to quantify genome-wide splicing efficiency from RNA-seq data. This tool aids in understanding gene expression dynamics and its role in human diseases like cancer.

Keywords:

Co-transcriptional splicing RNA-seq Splicing dynamics Splicing efficiency

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Last Updated: Oct 28, 2025

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

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Published on: December 9, 2016

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts

Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts

Published on: October 9, 2016

Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

Splicing is crucial for gene expression, removing introns to form mature RNA.
Misregulation of splicing is linked to various human diseases.
Quantifying splicing efficiency is vital for understanding gene expression dynamics and disease mechanisms.

Purpose of the Study:

Introduce SPLICE-q, a novel Python tool for rapid, genome-wide splicing efficiency quantification.
Provide a user-friendly method to analyze splicing efficiency implications in transcript processing.
Support research into the role of splicing in gene expression and disease.

Main Methods:

SPLICE-q utilizes strand-specific RNA-seq aligned reads.
Quantifies individual intron splicing efficiency.
Offers adjustable parameters for intron overlap with other genomic elements.

Main Results:

Demonstrated SPLICE-q's application in assessing intron excision dynamics in yeast and human nascent RNA-seq.
Showcased SPLICE-q's utility with total RNA-seq data from prostate cancer.
Validated SPLICE-q for detecting progressive splicing efficiency changes in time-course experiments.

Conclusions:

SPLICE-q is effective for detecting temporal changes in splicing efficiency.
The tool can provide insights into cancer progression beyond gene expression levels.
SPLICE-q is publicly available for research use.