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...

You might also read

Related Articles

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

Sort by

Same author

Research trends and hotspots in the field of electrical impedance tomography for mechanical ventilation: a bibliometric analysis.

Journal of thoracic disease·2024

Same author

Development and validation of a point-of-care nursing mobile tool to guide the diagnosis of malnutrition in hospitalized adult patients: a multicenter, prospective cohort study.

MedComm·2024

Same author

Biomass fuels related-PM<sub>2.5</sub> promotes lung fibroblast-myofibroblast transition through PI3K/AKT/TRPC1 pathway.

Ecotoxicology and environmental safety·2024

Same author

Utilization of EEG microstates as a prospective biomarker for assessing the impact of ketogenic diet in GLUT1-DS.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology·2024

Same author

Difference of carrier dynamics in a semiconductor saturable absorber mirror with and without B<sup>+</sup> ion-implantation.

Applied optics·2024

Same author

Immature persimmon residue as a novel biosorbent for efficient removal of Pb(II) and Cr(VI) from wastewater: Performance and mechanisms.

International journal of biological macromolecules·2024

Same journal

Interpretable machine learning for Parkinson's disease diagnosis, staging, and biological mechanism exploration: a multicenter analysis.

BioData mining·2026

Same journal

Learning a distance for the clustering of patients with amyotrophic lateral sclerosis.

BioData mining·2026

Same journal

Multi-domain feature fusion with variational mode decomposition and hybrid LightGBM-Logistic Regression for multi-class seizure classification.

BioData mining·2026

Same journal

Large-scale transcriptomic data mining using explainable XGBoost and SHAP reveals shared biomarkers and molecular mechanisms between type-2 diabetes and triple-negative breast cancer for drug repurposing.

BioData mining·2026

Same journal

AVSeg-XAI: Deep learning framework for A/V segmentation with vascular features reveals retinal oculomics as biomarker for cardiovascular disease.

BioData mining·2026

Same journal

Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.

BioData mining·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 23, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

An iteration normalization and test method for differential expression analysis of RNA-seq data.

Yan Zhou¹, Nan Lin², Baoxue Zhang³

¹Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA ; Key Laboratory for Applied Statistics of MOE and School of Mathematics and Statistics, Northeast Normal University, Changchun, 130024 Jilin Province, P. R. China.

|October 7, 2014

Summary

This summary is machine-generated.

A new gene expression normalization method, iterating median of M-values (IMM), improves differential expression detection accuracy. IMM outperforms existing methods in simulations and real-world data analysis for next-generation sequencing.

Keywords:

Expression level IMM Normalize RNA-seq TMM

More Related Videos

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Published on: May 16, 2020

Related Experiment Videos

Last Updated: Apr 23, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Published on: May 16, 2020

Area of Science:

Genomics and Bioinformatics
Computational Biology
Molecular Biology

Background:

Next-generation sequencing (NGS) generates massive biological and medical data.
Accurate analysis of NGS data requires robust statistical and computational methods.
Normalization of read counts is crucial to adjust for technical variations in sequencing depth.

Purpose of the Study:

To develop an improved normalization method for gene expression analysis.
To enhance the accuracy of detecting differentially expressed (DE) genes using NGS data.
To compare the performance of the new method against existing approaches like TMM.

Main Methods:

Development of the iterating median of M-values (IMM) normalization method.
Application of IMM for detecting differentially expressed genes.
Comparative analysis using simulation studies and real-world gene expression datasets.

Main Results:

The IMM method demonstrates improved accuracy in detecting differentially expressed genes compared to TMM.
Simulation studies confirm that IMM outperforms other sample normalization methods.
Analysis of real data shows that genes identified by IMM are more accurate than those identified by TMM alone; UNC5C gene linked to kidney cancer.

Conclusions:

The IMM normalization method offers a more accurate approach for DE gene detection in NGS data.
IMM provides superior performance in sample normalization, as validated by simulations and real data.
The method facilitates more reliable identification of biologically significant genes, such as UNC5C in kidney cancer.