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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: Jan 29, 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

Using BRIE to Detect and Analyze Splicing Isoforms in scRNA-Seq Data.

Yuanhua Huang¹, Guido Sanguinetti²

¹EMBL-European Bioinformatics Institute, Cambridgeshire, UK.

Methods in Molecular Biology (Clifton, N.J.)

|February 14, 2019

Summary

This summary is machine-generated.

Single-cell RNA sequencing (scRNA-seq) faces challenges in analyzing RNA splicing. We introduce BRIE, a Bayesian model, to accurately quantify splicing isoforms from scRNA-seq data.

Keywords:

Alternative splicing Bayesian model Isoform quantification Single-cell RNA-seq

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

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Detection of Alternative Splicing During Epithelial-Mesenchymal Transition

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Published on: October 9, 2014

Area of Science:

Computational Biology
Genomics
Molecular Biology

Background:

Single-cell RNA sequencing (scRNA-seq) measures transcriptional noise but struggles with RNA processing analysis.
Splicing isoform quantification is a key challenge in scRNA-seq data analysis.

Purpose of the Study:

To review challenges in splicing isoform quantification using scRNA-seq.
To introduce and illustrate the use of BRIE (Bayesian regression for isoform estimation) for accurate isoform quantification.

Main Methods:

Discussion of technical limitations in scRNA-seq for splicing analysis.
Introduction of BRIE, a Bayesian hierarchical model leveraging sequence features for informative priors.
Application of BRIE to a case study involving 130 mouse cells during gastrulation.

Main Results:

BRIE effectively addresses the limitations of scRNA-seq in resolving splicing isoform variability.
The model learns informative prior distributions from sequence features to improve quantification accuracy.
Demonstrated successful application of BRIE in a biological context (mouse gastrulation).

Conclusions:

BRIE offers a robust solution for accurate splicing isoform quantification in scRNA-seq data.
This approach enhances the ability to study RNA processing events at the single-cell level.
The findings facilitate deeper understanding of gene expression regulation during development.