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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...

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

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...

Pre-mRNA Processing: RNA Splicing

Pre-mRNA Processing: RNA Splicing

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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

ASAS-EGB: A statistical framework for estimating allele-specific alternative splicing events using transcriptome

Lili Dong¹, Jianan Wang¹, Guohua Wang¹

¹School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China.

Computers in Biology and Medicine

|May 10, 2023

Summary

This summary is machine-generated.

Alternative splicing (AS) is crucial in eukaryotes, impacting protein diversity and disease. Our new algorithm, ASAS-EGB, improves allele-specific AS analysis by using gene transcriptomes and Bayesian models for better cis-acting element identification.

Keywords:

Allele-specific alternative splicing Bayesian inference MCMC method RNA-Seq Transcriptome

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Published on: October 9, 2016

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Alternative splicing (AS) significantly expands protein diversity in eukaryotes.
AS plays a role in various diseases, including cancer.
Allele-specific AS (ASAS) analysis aids in identifying cis-acting elements by leveraging shared cellular environments between alleles.

Purpose of the Study:

To propose a novel statistical framework and algorithm, ASAS-EGB, for enhanced ASAS analysis.
To improve the inference of ASAS by utilizing comprehensive gene transcriptome information.

Main Methods:

Developed ASAS-EGB, a statistical framework and algorithm for ASAS analysis.
Employed Bayesian models and Markov Chain Monte Carlo (MCMC) methods for inference.
Incorporated both phased and non-phased single nucleotide polymorphisms (SNPs) within exons of gene isoforms.

Main Results:

ASAS-EGB demonstrated superior inferential performance compared to methods using only event isoforms.
The algorithm showed robustness across RNA-seq replicates of individual NA12878.
ASAS-EGB effectively utilizes gene isoform information for more accurate ASAS detection.

Conclusions:

ASAS-EGB provides a robust and improved approach for allele-specific alternative splicing analysis.
The framework's ability to leverage detailed genomic and transcriptomic data will be valuable for future research.
This method can reveal regulatory mechanisms within individual genomes.