Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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 microarray-based...

Ribosome Profiling

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...

Nonsense-mediated mRNA Decay

Nonsense-mediated mRNA Decay

The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
Usually, Upf3 binds to an Exon Junction Complex (EJC) at mRNA splice sites. If a ribosome fully translates the mRNA,...

mRNA Stability and Gene Expression

mRNA Stability and Gene Expression

The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
Cis-acting Elements involved in mRNA stability

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Structural Proteomics-Based Deciphering of Hydrophobic Packing Fingerprints Informing Protein Thermostability in TIM Barrels.

Journal of chemical information and modeling·2026

Same author

CPS: mapping physical coordinates to high-fidelity spatial transcriptomics via privileged multi-scale context distillation.

Bioinformatics (Oxford, England)·2026

Same author

Leveraging longitudinal data to boost statistical power for gene-environment interaction analysis.

Nature computational science·2026

Same author

Clinical efficacy and predictive model development for levetiracetam in children with newly diagnosed epilepsy of unknown etiology.

Translational pediatrics·2026

Same author

Response to: Comment on "Blink-induced epileptic negative myoclonus in CHD2-related epilepsy: A novel seizure semiology linked to visual transient hyperexcitability".

Epileptic disorders : international epilepsy journal with videotape·2026

Same author

Complex Fault Systems and Basin Evolution of the Bonan Area, Eastern China: Effects on the Sedimentation and Distribution of Source Rocks.

ACS omega·2026

Search research articles

Related Experiment Video

Updated: May 24, 2026

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Modeling RNA degradation for RNA-Seq with applications.

Lin Wan¹, Xiting Yan, Ting Chen

¹Molecular and Computational Biology Program, University of Southern California, Los Angeles, CA 90089, USA.

Biostatistics (Oxford, England)

|February 23, 2012

Summary

RNA sequencing (RNA-Seq) analysis accuracy improves by modeling RNA degradation. This new method estimates transcript expression and RNA degradation rates, overcoming biases from non-uniform read distribution.

More Related Videos

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Published on: December 9, 2022

AQRNA-seq for Quantifying Small RNAs

AQRNA-seq for Quantifying Small RNAs

Published on: February 2, 2024

Related Experiment Videos

Last Updated: May 24, 2026

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Published on: December 9, 2022

AQRNA-seq for Quantifying Small RNAs

AQRNA-seq for Quantifying Small RNAs

Published on: February 2, 2024

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

RNA sequencing (RNA-Seq) is crucial for biological and biomedical research, widely used to estimate transcript abundance.
Current RNA-Seq analysis methods often assume uniform short-read distribution, which can lead to inaccurate expression level estimations.
Non-uniform short-read distribution, influenced by factors like RNA degradation, poses a significant challenge in accurate transcript abundance quantification.

Purpose of the Study:

To develop a novel approach for quantifying short-read non-uniformity by modeling RNA degradation.
To introduce a statistical method for estimating transcript expression levels and RNA degradation rates.
To enhance the accuracy of transcript isoform expression estimation in RNA-Seq data analysis.

Main Methods:

Developed a new computational approach to model RNA degradation for quantifying short-read non-uniformity.
Implemented a statistical method based on the RNA degradation model to estimate transcript expression levels and RNA degradation rates.
Validated the method's performance in improving transcript isoform expression estimation accuracy.

Main Results:

The RNA degradation model accurately fits RNA-Seq data, effectively capturing short-read non-uniformity.
The developed statistical method significantly improves the accuracy of transcript isoform expression estimation.
Estimated RNA degradation rates are consistent across different samples, experiments, and platforms, and are independent of RNA length.

Conclusions:

Modeling RNA degradation provides an effective alternative to address biases from non-uniform short-read distribution in RNA-Seq.
The new method offers a robust approach for accurate transcript expression level and RNA degradation rate estimation.
This work contributes to more reliable and precise analyses of gene expression using RNA-Seq data.