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RNA Splicing

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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: Jul 23, 2025

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

Counting pseudoalignments to novel splicing events.

Luka Borozan¹, Francisca Rojas Ringeling^2,3, Shao-Yen Kao⁴

¹Department of Mathematics, Josip Juraj Strossmayer University of Osijek, Osijek 31000, Croatia.

Bioinformatics (Oxford, England)

|July 11, 2023

Summary

A new computational method, fortuna, efficiently identifies novel alternative splicing events from RNA sequencing data. This method accelerates transcript analysis and improves the detection of disease-associated splicing variations.

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Area of Science:

Computational Biology
Genomics
Molecular Biology

Background:

Alternative splicing (AS) generates transcript diversity but its dysregulation is linked to diseases.
Existing alignment-free methods miss novel splicing events, while alignment-based methods are computationally intensive.
Identifying novel, disease-specific splicing events remains a challenge.

Purpose of the Study:

To develop a computationally efficient method for identifying novel alternative splicing events.
To overcome the limitations of existing alignment-free and alignment-based AS quantification methods.
To accurately quantify transcripts and detect aberrant splicing in disease.

Main Methods:

Developed 'fortuna', a method that generates transcript fragments from known splice sites.
Utilized pseudoalignment with kallisto to efficiently count splicing units from equivalence classes.
Applied fortuna to synthetic and real RNA-seq data, including autism spectrum disorder patient samples.

Main Results:

Fortuna is approximately 7x faster than traditional align-and-count methods.
Analyzed ~300 million RNA-seq reads in 15 minutes using four threads.
Improved detection of novel junctions, mismatches, and aberrant splicing events in autism patients.
Identified novel tissue-specific splicing events in Drosophila.

Conclusions:

Fortuna offers a significant speedup for alternative splicing analysis.
The method enhances the discovery of novel and disease-associated splicing events.
Fortuna provides a valuable tool for transcriptomic studies, particularly in disease research.