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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...

Alternative RNA Splicing

Alternative RNA Splicing

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...

RNA Splicing

RNA Splicing

Pre-mRNA Processing: RNA Splicing

Pre-mRNA Processing: RNA Splicing

Pre-mRNA Processing

Pre-mRNA Processing

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Related Experiment Video

Updated: May 2, 2026

Detection of Alternative Splicing During Epithelial-Mesenchymal Transition

Detection of Alternative Splicing During Epithelial-Mesenchymal Transition

Published on: October 9, 2014

Predicting alternative splicing.

Yoseph Barash¹, Jorge Vaquero Garcia

¹Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA.

Methods in Molecular Biology (Clifton, N.J.)

|February 20, 2014

Summary

This summary is machine-generated.

This study introduces a machine-learning approach to predict alternative splicing outcomes from primary RNA transcripts. The method identifies RNA regulatory features to forecast exon inclusion levels, offering insights into splicing mechanisms.

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Area of Science:

Molecular Biology
Computational Biology
Genomics

Background:

Alternative splicing is a complex regulatory process influenced by spliceosome units, RNA motifs, and secondary structures.
While splicing can be stochastic, leading to transcript variability, it also exhibits robust, conserved changes across developmental stages and tissues.

Purpose of the Study:

To develop a predictive method for alternative splicing outcomes based on cellular context and primary transcripts.
To utilize machine learning to understand the underlying mechanisms governing alternative splicing regulation.

Main Methods:

Extracting RNA regulatory features from genomic sequences of exons and introns.
Defining target values using experimental measurements of exon inclusion.
Learning a splicing model to predict exon inclusion levels from identified RNA features.
Evaluating the accuracy of the learned predictive model.

Main Results:

The study demonstrates the feasibility of using machine learning to predict splicing outcomes.
Identified key RNA features that influence alternative splicing decisions.
Quantified the accuracy of the developed splicing prediction model.

Conclusions:

Machine learning offers a powerful framework for dissecting alternative splicing mechanisms.
Predictive models can provide valuable insights into the determinants of splicing regulation.
This approach can help bridge the gap between genomic sequence and functional splicing outcomes.