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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.
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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...

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Related Experiment Video

Updated: Feb 8, 2026

Detection of Alternative Splicing During Epithelial-Mesenchymal Transition

Detection of Alternative Splicing During Epithelial-Mesenchymal Transition

Published on: October 9, 2014

COSSMO: predicting competitive alternative splice site selection using deep learning.

Hannes Bretschneider^1,2, Shreshth Gandhi¹, Amit G Deshwar^1,3

¹Deep Genomics Inc, Toronto, Canada.

Bioinformatics (Oxford, England)

|June 29, 2018

Summary

This summary is machine-generated.

We developed a new model, COSSMO, to predict alternative splicing events by accounting for competitive splice site selection. COSSMO accurately predicts splice site usage and learns key sequence motifs.

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Engineering Artificial Factors to Specifically Manipulate Alternative Splicing in Human Cells

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Published on: April 26, 2017

Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Alternative splicing is regulated by the competitive selection of splice sites.
The probability of splice site usage is influenced by neighboring site strengths.

Purpose of the Study:

To introduce the competitive splice site model (COSSMO) for predicting alternative splicing.
To model alternative splicing as a choice between 5' donor or 3' acceptor sites.

Main Methods:

Developed four distinct neural network architectures (CNN, communication layers, LSTM, ResNet) to learn motifs from sequence data.
Constructed a novel dataset using genome annotations and RNA-Seq data for model training.

Main Results:

COSSMO predicts the most frequent splice site with 70% accuracy on test data.
The model achieves an R2 of 0.6 in modeling the percent selected index (PSI) distribution.
Learned motifs correspond to consensus splice site sequences and known splicing factors.

Conclusions:

COSSMO effectively models competitive splice site selection.
The model demonstrates high accuracy in predicting splice site usage and PSI distribution.
COSSMO provides insights into splicing factor recognition and sequence motifs.