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...

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...

Real Time RT-PCR

Real Time RT-PCR

Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...

You might also read

Related Articles

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

Sort by

Same author

MAJIQ-CLIN: A novel tool to help identify Mendelian disease-causing variants from RNA-Seq data.

Genetics in medicine : official journal of the American College of Medical Genetics·2026

Same author

Phenotype Refinement Influences GWAS-Implicated Variant Effect Sizes for Insomnia.

Journal of sleep research·2026

Same author

Quantifying sleep wake rhythms in the hospital environment with digital technologies.

NPJ digital medicine·2026

Same author

TracMyAir: Smartphone-enabled spatiotemporal estimates for inhaled doses of particulate matter and ozone to personalize health outcomes.

medRxiv : the preprint server for health sciences·2026

Same author

Depression of tryptophan may contribute to adverse effects of naproxen.

Nature communications·2026

Same author

Age and the Diurnal Oscillatory Features of the Human Chronobiome.

medRxiv : the preprint server for health sciences·2026

Search research articles

Related Experiment Video

Updated: Nov 4, 2025

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Published on: February 2, 2024

Comparative evaluation of full-length isoform quantification from RNA-Seq.

Dimitra Sarantopoulou^1,2, Thomas G Brooks¹, Soumyashant Nayak¹

¹Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.

BMC Bioinformatics

|May 26, 2021

Summary

This summary is machine-generated.

Accurate full-length isoform quantification from RNA sequencing (RNA-Seq) is challenging due to short reads. While some methods perform well on idealized data, their advantage diminishes on realistic datasets, suggesting selective use.

Keywords:

Benchmarking Isoform quantification Pseudo-alignment RNA-seq Short reads Simulated data

More Related Videos

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Published on: May 9, 2017

Related Experiment Videos

Last Updated: Nov 4, 2025

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Published on: February 2, 2024

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Published on: May 9, 2017

Area of Science:

Transcriptomics
Bioinformatics
Computational Biology

Background:

Full-length isoform quantification from RNA sequencing (RNA-Seq) is a critical but challenging task in transcriptomics.
The primary difficulty arises from the discrepancy between long RNA transcripts and short RNA-Seq reads.

Purpose of the Study:

To systematically compare the accuracy of various isoform quantification methods using simulated benchmarking data.
To assess the impact of quantification accuracy on differential expression analysis.
To identify key data characteristics influencing quantification performance.

Main Methods:

Utilized simulated benchmarking data with realistic properties like polymorphisms, intron signals, and non-uniform coverage.
Included genome, transcriptome, and pseudo-alignment-based quantification methods.
Incorporated a simple approach as a baseline for comparison.

Main Results:

Salmon, kallisto, RSEM, and Cufflinks showed high accuracy on idealized data but limited improvement over a simple approach on realistic data.
Transcript length and sequence compression complexity were identified as the most impactful structural parameters for quantification accuracy.
The influence of incomplete genome annotation on method performance was also evaluated.

Conclusions:

Current quantification methods demonstrate significant divergence from true values on realistic data.
The performance gains of advanced methods over simpler approaches are not always substantial.
Selective application of full-length isoform quantification and isoform-level differential expression analysis is recommended.