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...

You might also read

Related Articles

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

Sort by

Same author

Multi-omics dissection of steviol glycoside synthesis reveals haplotype-linked specialization of UGT76G genes in Stevia rebaudiana.

The New phytologist·2026

Same author

[A Clinical Study of Short-Term Outcomes of High-Difficulty Hepatobiliary Pancreatic Surgery in Elderly Patients Aged 80 and Older at Our Hospital].

Gan to kagaku ryoho. Cancer & chemotherapy·2026

Same author

[A Case of Gallbladder Cancer Diagnosed by Pathology after Full-Thickness Cholecystectomy and Death Due to Early Recurrence].

Gan to kagaku ryoho. Cancer & chemotherapy·2026

Same author

[Four Stitches Duct-to-Mucosa Pancreatojejunostomy].

Gan to kagaku ryoho. Cancer & chemotherapy·2026

Same author

Multicenter cohort study of the effect of Crinone® progesterone vaginal gel alone to prepare the endometrium for frozen-thawed blastocyst transfer in a hormone replacement cycle.

The journal of obstetrics and gynaecology research·2025

Same author

National Survey on the Status of Embryo Freezing for Fertility Preservation in Japan.

Journal of adolescent and young adult oncology·2025

Search research articles

Related Experiment Video

Updated: Jun 13, 2026

Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response

Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response

Published on: August 10, 2017

SRscore: An R Package for Quantifying Gene Stress Responsiveness Across Multiple Transcriptome Datasets Using

Yusuke Fukuda¹, Atsushi Fukushima^1,2

¹Graduate School of Life and Environmental Sciences, Kyoto Prefectural University, Kyoto, Kyoto, Japan.

Genes to Cells : Devoted to Molecular & Cellular Mechanisms

|February 24, 2026

Summary

This summary is machine-generated.

We developed SRscore, an R package for reproducible transcriptome meta-analysis to identify stress-responsive genes. This tool streamlines analysis across species, aiding gene function discovery.

Keywords:

RNA‐seq analysis differential gene expression microarray reproducible workflow transcriptome meta‐analysis transcriptomics

More Related Videos

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

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

Related Experiment Videos

Last Updated: Jun 13, 2026

Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response

Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response

Published on: August 10, 2017

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

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

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Transcriptome meta-analysis is vital for understanding gene functions, particularly stress-responsive genes.
The Stress Response score (SRscore) was previously developed to quantify gene stress responsiveness using expression variability.
Previous SRscore application was limited to specific species and required a dedicated tool for broader use.

Purpose of the Study:

To introduce SRscore, an R package for streamlined and reproducible transcriptome meta-analyses.
To facilitate the identification and study of stress-responsive genes across diverse species.
To enhance the integration of meta-analysis outputs with downstream bioinformatics tools.

Main Methods:

Development of the SRscore R package with a three-step workflow: comparison group pairing, SRratio calculation, and SRscore computation.
Inclusion of curated datasets and metadata handling functions for simplified analysis.
Ensuring compatibility with other R/Bioconductor packages for visualization and enrichment analysis.

Main Results:

The SRscore R package provides a user-friendly, reproducible, and error-minimized workflow for transcriptome meta-analysis.
The package effectively calculates the Stress Response score (SRscore) and expression change ratio (SRratio).
SRscore outputs are designed for seamless integration with existing bioinformatics analysis pipelines.

Conclusions:

The SRscore R package significantly advances transcriptome meta-analysis for stress-responsive gene research.
This tool broadens the applicability of SRscore across species and enhances reproducibility in genomic studies.
SRscore facilitates deeper insights into gene functions related to environmental stress responses.