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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...

RNA Splicing

RNA Splicing

Pre-mRNA Processing: RNA Splicing

Pre-mRNA Processing: 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...

Alternative RNA Splicing

Alternative RNA Splicing

Chromatin Structure and RNA Splicing

Chromatin Structure and RNA Splicing

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Related Experiment Video

Updated: Apr 24, 2026

Engineering Artificial Factors to Specifically Manipulate Alternative Splicing in Human Cells

Engineering Artificial Factors to Specifically Manipulate Alternative Splicing in Human Cells

Published on: April 26, 2017

Splicing code modeling.

Yoseph Barash¹, Jorge Vaquero-Garcia

¹Department of Genetics, University of Pennsylvania, University Park, PA, USA, yosephb@mail.med.upenn.edu.

Advances in Experimental Medicine and Biology

|September 10, 2014

Summary

This summary is machine-generated.

Researchers are deciphering the pre-mRNA splicing code using machine learning. This approach analyzes RNA-binding protein (RBP) data to predict splicing outcomes and uncover regulatory mechanisms.

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Last Updated: Apr 24, 2026

Engineering Artificial Factors to Specifically Manipulate Alternative Splicing in Human Cells

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Published on: April 26, 2017

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Using the E1A Minigene Tool to Study mRNA Splicing Changes

Using the E1A Minigene Tool to Study mRNA Splicing Changes

Published on: April 22, 2021

Area of Science:

Molecular Biology
Computational Biology
Genomics

Background:

Pre-mRNA splicing regulation is complex, involving numerous cis and trans elements whose interactions form a 'splicing code'.
Variations in splicing across tissues and developmental stages, and links to diseases, underscore the importance of understanding this code.
The complexity and context-specific nature of splicing regulation have historically challenged efforts to decipher this code.

Purpose of the Study:

To explore the application of machine learning methods for inferring computational models of the splicing code.
To demonstrate how these models can predict splicing outcomes and identify novel regulatory mechanisms.
To provide a practical tool (AVISPA web tool) for RNA splicing experts to study exon regulation.

Main Methods:

Utilizing high-throughput experiments that measure mRNA expression at exonic resolution.
Analyzing binding locations of RNA-binding proteins (RBPs).
Developing and applying machine learning algorithms to infer regulatory models of splicing.

Main Results:

Machine learning models can be inferred from experimental data to represent the splicing code.
These models enable predictions of splicing outcomes for unmeasured genomic sequences or conditions.
The models can be interrogated to discover new splicing regulatory mechanisms.

Conclusions:

Machine learning offers a powerful approach to decipher the complex splicing code.
Computational models derived from high-throughput data can predict and reveal splicing regulation.
The AVISPA web tool facilitates the application of these methods by non-computational experts to study exon regulation.