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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.
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 Regulates pre-mRNA Processing

Chromatin Structure Regulates pre-mRNA Processing

In eukaryotic cells, nascent mRNA transcripts need to undergo many post-transcriptional modifications to reach the cell cytoplasm and translate into functional proteins. For a long time, transcription and pre-mRNA processing were considered two independent events that occur sequentially in the cell. However, it has now been well established that transcription and pre-mRNA processing are two simultaneous processes that are precisely regulated inside the cell.
The chromatin structure, especially...

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...

Chromatin Structure and RNA Splicing

Chromatin Structure and RNA Splicing

Epigenetic Regulation

Epigenetic Regulation

Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
X-chromosome...

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Related Experiment Video

Updated: Apr 4, 2026

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

A computational method for studying the relation between alternative splicing and DNA methylation.

Zejun Zheng¹, Xiaona Wei², Andreas Hildebrandt³

¹Bioinformatics Institute, Singapore 138671, Singapore.

Nucleic Acids Research

|September 15, 2015

Summary

Intragenic DNA methylation influences alternative splicing complexity. This study introduces a computational method to identify DNA methylation patterns and correlate them with exon inclusion, aiding future research.

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Area of Science:

Molecular Biology
Genomics
Bioinformatics

Background:

Alternative splicing significantly expands eukaryotic transcriptome and proteome diversity.
Intragenic DNA methylation is increasingly implicated in regulating alternative splicing.
The precise mechanisms linking DNA methylation to alternative splicing remain largely unknown.

Purpose of the Study:

To develop a computational technique for identifying stable intragenic DNA methylation patterns.
To correlate these identified methylation patterns with exon inclusion propensity.
To provide a tool for analyzing large-scale datasets in future research.

Main Methods:

Utilized dynamic time warping (DTW) for robust similarity measurement of methylation profiles.
Employed a self-organizing map approach for grouping detected methylation patterns.
Applied the method to available datasets to identify correlations with exon splicing.

Main Results:

Identified stable DNA methylation patterns within gene bodies.
Demonstrated non-trivial correlations between specific methylation patterns and exon inclusion rates.
The developed software is optimized for efficient processing of large datasets.

Conclusions:

Stable intragenic DNA methylation patterns are associated with alternative splicing regulation.
The computational approach provides a novel method for investigating this complex regulatory mechanism.
Further validation on larger datasets is necessary to enhance prediction reliability.