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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

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

Pre-mRNA Processing: RNA Splicing

Pre-mRNA Processing: RNA Splicing

Alternative RNA Splicing

Alternative 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...

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Related Experiment Video

Updated: May 3, 2026

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Published on: December 9, 2016

A multi-split mapping algorithm for circular RNA, splicing, trans-splicing and fusion detection.

Steve Hoffmann, Christian Otto, Gero Doose

|February 12, 2014

Summary

This summary is machine-generated.

This study presents a new algorithm for detecting splice junctions in RNA sequencing data, improving the identification of non-standard RNA molecules like gene fusions and circular RNAs.

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Last Updated: May 3, 2026

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

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Published on: December 9, 2016

Identification of Circular RNAs using RNA Sequencing

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Published on: November 14, 2019

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

Area of Science:

Bioinformatics
Genomics
Molecular Biology

Background:

High-throughput sequencing (RNA-seq) primarily detects conventionally spliced messenger RNAs (mRNAs).
Non-standard RNA structures, including gene fusions, circularization, and trans-splicing, are often overlooked in current analyses.
Accurate detection of diverse splice junctions is crucial for comprehensive transcriptome analysis.

Purpose of the Study:

To introduce a novel, unbiased algorithm for detecting splice junctions from single-end cDNA sequences.
To develop a method capable of identifying multi-junction splice structures.
To improve the detection of non-standard RNA molecules in RNA-seq data.

Main Methods:

Development of an unbiased algorithm for splice junction detection from single-end cDNA sequences.
Accommodation of multi-junction splice structures within the algorithm.
Integration of the algorithm into the segemehl mapping tool.

Main Results:

The novel algorithm demonstrates comparable performance to existing tools for conventionally spliced mRNAs.
The method achieves up to a 40% increase in recall for reads with multiple splits, trans-splicing, and circular products.
Systematic outperformance of competing tools on complex and non-standard RNA structures.

Conclusions:

The developed algorithm significantly enhances the detection of splice junctions, particularly for non-standard RNA species.
This advancement offers a more comprehensive analysis of transcriptomes by including previously neglected RNA structures.
The integration into segemehl provides a practical tool for researchers studying complex RNA splicing events.