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...

You might also read

Related Articles

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

Sort by

Same author

The impact of a three-dimensional interconnected continuity of care intervention on self-care capacity, negative emotional states, and quality of life in patients with ostomies.

Frontiers in public health·2026

Same author

Identification of tumor-associated antigens with multi-cancer therapeutic potential.

Frontiers in immunology·2026

Same author

Using personal mobile technology to identify obstructive sleep apnea in children with Down syndrome (UPLOAD).

Sleep medicine·2026

Same author

Coupled Effects of Injection Pressure and Coal Moisture on Gas Pressure and Concentration Distribution during Gas Displacement.

Langmuir : the ACS journal of surfaces and colloids·2026

Same author

Real-world comparison of shape-sensing robotic-assisted bronchoscopy and virtual bronchoscopic navigation for peripheral pulmonary lesions: a propensity score-matched study.

Respiratory research·2026

Same author

Pt-Y supported on magnesium-aluminium composite oxide catalysts for highly selective synthesis of 1,2-pentanediol from furfuryl alcohol under mild conditions.

RSC advances·2026

Same journal

Mapping the 3D Chromosome Organization of a Biosynthetic Gene Cluster by Capture Hi-C (CHi-C).

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Mapping the 3D Chromosome Organization of Streptomyces by Hi-C.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

CUT&Tag Epigenomic Profiling of Biosynthetic Gene Clusters in Arabidopsis thaliana.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Rhizobium rhizogenes-Mediated Hairy Root Transformation Protocol for Lotus japonicus and Other Legumes.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Characterization of Bioactive Saponins from Sea Cucumbers.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Methods for Functional Validation of Terpenoid Metabolic Clusters in Nicotiana benthamiana and Aspergillus oryzae.

Methods in molecular biology (Clifton, N.J.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 18, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

Quality control of RNA-seq experiments.

Xing Li¹, Asha Nair, Shengqin Wang

¹Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.

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

|January 12, 2015

Summary

This summary is machine-generated.

High-throughput sequencing (RNA-seq) offers comprehensive transcriptome analysis but requires rigorous quality control. Assessing metrics like sequence quality and biases is crucial for reliable RNA-seq data interpretation.

More Related Videos

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

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Published on: September 13, 2018

Related Experiment Videos

Last Updated: Apr 18, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

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

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Published on: September 13, 2018

Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

High-throughput sequencing (RNA-seq) provides a comprehensive view of the transcriptome, surpassing microarrays.
RNA-seq theoretically allows precise identification and quantification of all RNA species.
The RNA-seq workflow involves multiple complex steps, each susceptible to introducing biases and errors.

Purpose of the Study:

To highlight the critical importance of quality assessment in RNA-seq data.
To discuss common quality control metrics for RNA-seq experiments.
To emphasize the impact of biases on downstream analysis and interpretation.

Main Methods:

Review of established quality control metrics for RNA-seq.
Discussion of potential biases including GC bias and PCR bias.
Examination of contamination sources like rRNA and mitochondrial RNA.

Main Results:

RNA-seq data can be affected by operational errors and intrinsic biases.
Comprehensive quality control is essential for accurate downstream analyses.
Key metrics include sequence quality, depth, duplication rates, alignment, nucleotide composition, and coverage uniformity.

Conclusions:

Rigorous quality control is the most critical step for reliable RNA-seq data.
Understanding and mitigating biases are essential for valid transcriptome analysis.
Effective quality assessment ensures the integrity of RNA-seq results and interpretations.