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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...

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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Exon Recombination

Exon Recombination

The evolution of new genes is critical for speciation. Exon recombination, also known as exon shuffling or domain shuffling, is an important means of new gene formation. It is observed across vertebrates, invertebrates, and in some plants such as potatoes and sunflowers. During exon recombination, exons from the same or different genes recombine and produce new exon-intron combinations, which might evolve into new genes.
Exon shuffling follows “splice frame rules.” Each exon...

Pre-mRNA Processing: Modification of pre-mRNA Ends

Pre-mRNA Processing: Modification of pre-mRNA Ends

In eukaryotic cells, transcripts made by RNA polymerase are modified and processed before exiting the nucleus. Unprocessed RNA is called precursor mRNA or pre-mRNA to distinguish it from mature mRNA.
Once about 20-40 ribonucleotides have been joined together by RNA polymerase, a group of enzymes adds a cap to the 5' end of the growing transcript. In this process, a 5' phosphate is replaced by modified guanosine that has a methyl group attached (7-methyl guanosine). This 5' cap helps...

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Related Experiment Video

Updated: Jul 31, 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

Aberrant splicing prediction across human tissues.

Nils Wagner^1,2, Muhammed H Çelik^1,3, Florian R Hölzlwimmer¹

¹School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.

Nature Genetics

|May 4, 2023

Summary

This summary is machine-generated.

This study improves the detection of aberrant splicing, a cause of genetic disorders, by integrating RNA data. The new AbSplice model significantly enhances precision in identifying disease-causing variants across human tissues.

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Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Aberrant splicing is a key driver of genetic disorders.
Current detection methods are limited to accessible tissues.
DNA-based models for predicting splicing effects are limited in tissue-specific accuracy.

Purpose of the Study:

To develop a more accurate method for detecting tissue-specific aberrant splicing.
To improve the identification of noncoding loss-of-function variants.
To enhance genetic diagnostics by assessing splicing impacts.

Main Methods:

Generated an aberrant splicing benchmark dataset from 8.8 million rare variants across 49 human tissues (GTEx).
Mapped and quantified tissue-specific splice site usage and modeled isoform competition.
Integrated RNA-sequencing data from accessible tissues into the AbSplice model.

Main Results:

State-of-the-art DNA-based models achieved only 12% precision at 20% recall for tissue-specific splicing.
The developed method increased precision threefold by analyzing splice site usage and isoform competition.
The AbSplice model, integrating RNA data, reached 60% precision, validated in independent cohorts.

Conclusions:

The AbSplice model significantly improves the precision of aberrant splicing detection across diverse human tissues.
This advancement aids in identifying noncoding loss-of-function variants and enhances genetic diagnostics.
The findings offer a more comprehensive approach to understanding splicing defects in genetic disorders.