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

Identifying Relevant Covariates in RNA-seq Analysis by Pseudo-Variable Augmentation.

Journal of agricultural, biological, and environmental statistics·2026

Same author

Testing the effects of segmented crowdsource-selected messages to improve intentions to follow colorectal cancer screening recommendations: study protocol for a randomized controlled trial.

BMC public health·2026

Same author

Light cues induce protective anticipation of environmental water loss in terrestrial bacteria.

Proceedings of the National Academy of Sciences of the United States of America·2023

Same author

Adjusting for gene-specific covariates to improve RNA-seq analysis.

Bioinformatics (Oxford, England)·2023

Same author

High-Throughput Field Plant Phenotyping: A Self-Supervised Sequential CNN Method to Segment Overlapping Plants.

Plant phenomics (Washington, D.C.)·2023

Same author

"Just right" combinations of adjuvants with nanoscale carriers activate aged dendritic cells without overt inflammation.

Immunity & ageing : I & A·2023

Same journal

OmicsTransformer: Self-Supervised Masked Consistency and Uncertainty-Aware Fusion for Robust Multi-Omics Prediction.

Bioinformatics (Oxford, England)·2026

Same journal

Computational Tool Choice Impacts CRISPR Spacer-Proto spacer Detection.

Bioinformatics (Oxford, England)·2026

Same journal

ARISE: RNA-Anchored Shared-Edge Topology and Hierarchical Fusion for Spatial Multi-Omics Integration.

Bioinformatics (Oxford, England)·2026

Same journal

Interactive exploration of biobank-scale ancestral recombination graphs with Lorax.

Bioinformatics (Oxford, England)·2026

Same journal

PepMCP: A Graph-Based Membrane Contact Probability Predictor for Membrane-Lytic Antimicrobial Peptides.

Bioinformatics (Oxford, England)·2026

Same journal

ARGscape: A modular, interactive tool for manipulation of spatiotemporal ancestral recombination graphs.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 20, 2025

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

rmRNAseq: differential expression analysis for repeated-measures RNA-seq data.

Yet Nguyen¹, Dan Nettleton²

¹Department of Mathematics and Statistics, Old Dominion University, Norfolk, VA 23529, USA.

Bioinformatics (Oxford, England)

|May 26, 2020

Summary

This summary is machine-generated.

This study introduces a new method for RNA-seq differential expression analysis in repeated-measures designs. It accurately accounts for temporal correlations within experimental units, improving analysis accuracy.

More Related Videos

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

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: Dec 20, 2025

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

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

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
Bioinformatics
Statistical Genetics

Background:

Next-generation sequencing and RNA-seq enable complex experimental designs, including those with repeated measures.
RNA samples from the same experimental unit at multiple time points exhibit temporal correlations in read counts.
Existing RNA-seq differential expression analysis methods often fail to adequately address within-unit correlations in repeated-measures designs.

Purpose of the Study:

To develop a novel method for RNA-seq differential expression analysis that properly accounts for within-unit temporal correlations in repeated-measures designs.
To provide a robust statistical framework for analyzing gene expression data from longitudinal studies.

Main Methods:

Utilized normalized log-transformed counts and precision weights within a general linear model.
Incorporated a continuous autoregressive structure to model temporal correlations among observations within experimental units.
Employed parametric bootstrap for differential expression inference.

Main Results:

Simulation studies demonstrated the superiority of the proposed method compared to alternatives that ignore within-unit correlations.
The method effectively handles the temporal dependency inherent in repeated-measures RNA-seq data.
The developed approach provides more accurate differential expression results for complex designs.

Conclusions:

The proposed method offers a significant improvement for RNA-seq differential expression analysis in repeated-measures experiments.
Accurate modeling of within-unit correlations is crucial for reliable gene expression analysis in longitudinal studies.
The rmRNAseq R package provides a practical implementation for researchers.