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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...

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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.
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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: Aug 5, 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

Single cell and spatial alternative splicing analysis with long read sequencing.

Yuntian Fu¹, Heonseok Kim², Jenea I Adams¹

¹Graduate Program in Genomics and Computational Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.

Research Square

|March 30, 2023

Summary

This summary is machine-generated.

Long-read sequencing enables alternative splicing analysis but faces challenges. We developed Longcell, a computational tool for accurate isoform quantification in single-cell and spatial data, revealing widespread intra-cell splicing heterogeneity.

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Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Long-read sequencing is valuable for alternative splicing analysis.
Technical and computational hurdles limit single-cell and spatial resolution.
High error rates in long reads impact barcode recovery and isoform accuracy.

Purpose of the Study:

To develop a robust statistical framework and computational pipeline for accurate isoform quantification from single-cell and spatial long-read sequencing data.
To address challenges in cell barcode and UMI recovery, and correct for read truncation and mapping errors.
To rigorously quantify splicing variation within and between cells/spots and detect changes in splicing distributions.

Main Methods:

Developed Longcell, a computational pipeline for cell/spot barcode extraction, UMI recovery, and error correction.
Implemented a statistical model to account for varying read coverage and quantify splicing diversity.
Applied Longcell to single-cell, spatial, and perturbation-based long-read sequencing datasets.

Main Results:

Longcell enables computationally efficient and accurate isoform quantification.
Intra-cell splicing heterogeneity is ubiquitous for highly expressed genes.
Concordant signals were observed between single-cell and spatial long-read data in colorectal cancer metastasis.
Identified validated regulatory targets of splicing factors.

Conclusions:

Longcell overcomes technical challenges in long-read sequencing for splicing analysis.
It provides a rigorous method for quantifying splicing variation at single-cell and spatial resolutions.
The framework reveals widespread intra-cell splicing heterogeneity and aids in identifying regulatory relationships.