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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...

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

Pre-mRNA Processing: RNA Splicing

Pre-mRNA Processing: RNA Splicing

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Related Experiment Video

Updated: Apr 16, 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

Mapping Splicing Quantitative Trait Loci in RNA-Seq.

Cheng Jia¹, Yu Hu¹, Yichuan Liu¹

¹Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.

Cancer Informatics

|March 4, 2015

Summary

This summary is machine-generated.

Random effects meta-regression is the most powerful method for analyzing splicing quantitative trait loci (sQTLs) from RNA-Seq data. This approach offers high power and low false discovery rates for identifying sQTLs, aiding cancer research.

Keywords:

RNA-Seq alternative splicing quantitative trait loci

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Last Updated: Apr 16, 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 Alternative Splicing and Polyadenylation in RNA-seq Data

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Published on: June 24, 2021

Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts

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Published on: October 9, 2016

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Alternative splicing generates mRNA diversity, enabling diverse protein production from single genes.
Dysregulation of alternative splicing in cancer contributes to aberrant protein production and disease progression.
Understanding splicing misregulation is crucial for developing novel cancer therapeutic targets.

Purpose of the Study:

To evaluate and compare statistical methods for splicing quantitative trait loci (sQTL) analysis using RNA-Seq data.
To identify the most reliable and powerful statistical approach for sQTL detection.

Main Methods:

Three statistical methods were evaluated: random effects meta-regression, beta regression, and generalized linear mixed effects model.
Exon-inclusion levels were estimated using the PennSeq algorithm, which accounts for paired-end reads and sequencing coverage.
Methods were compared using simulated and real RNA-Seq datasets against the GLiMMPS method.

Main Results:

Random effects meta-regression demonstrated superior reliability and power compared to GLiMMPS.
This method identified sQTLs with low false discovery rates and high statistical power.
The study provides a comparative analysis of statistical approaches for sQTL analysis.

Conclusions:

Random effects meta-regression is recommended as the most effective method for sQTL analysis in RNA-Seq studies.
The findings will guide researchers in selecting optimal statistical tools for sQTL analysis.
Accurate sQTL identification can advance the understanding of alternative splicing in disease contexts.