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

Predicting subclinical atherosclerosis in adults with type 1 diabetes using cardiovascular risk models: a cross‑sectional study from Italy.

Cardiovascular diabetology·2026

Same author

Disease-Associated Remodeling of m6A RNA Methylation in Human Interstitial Cells From Fibro-Calcific Aortic Valves.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology·2026

Same author

Circulating cytokines as triggers of endothelial dysfunction and sex-specific interstitial cell response in fibrocalcific aortic valve disease.

International journal of biological sciences·2026

Same author

Inflammatory Proteomic Heterogeneity Beyond Glycemia Status in Severe Obesity.

International journal of molecular sciences·2026

Same author

Mechanical load inhibits cancer growth in mouse and human hearts.

Science (New York, N.Y.)·2026

Same author

Leptin Receptor Fibroblasts Are Preferential Contributors to Cardiac Fibrosis.

Circulation research·2026

Search research articles

Related Experiment Video

Updated: Feb 17, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

DaMiRseq-an R/Bioconductor package for data mining of RNA-Seq data: normalization, feature selection and

Mattia Chiesa¹, Gualtiero I Colombo¹, Luca Piacentini¹

¹Immunology and Functional Genomics Unit, Centro Cardiologico Monzino, IRCCS, 20138 Milan, Italy.

Bioinformatics (Oxford, England)

|December 14, 2017

Summary

This summary is machine-generated.

DaMiRseq provides a robust R framework for analyzing high-dimensional RNA-Seq data. This tool effectively reduces noise and bias, enabling accurate biomarker discovery and classification for transcriptome profiling.

More Related Videos

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

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

Related Experiment Videos

Last Updated: Feb 17, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

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

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

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

High-throughput transcriptome profiling using RNA-Seq is crucial for differential gene expression analysis and biomarker discovery.
Analyzing high-dimensional genomic data presents significant computational challenges.

Purpose of the Study:

To introduce DaMiRseq, a flexible R package designed for systematic analysis of RNA-Seq data.
To provide a framework for noise reduction, feature selection, and accurate classification in high-dimensional genomic datasets.

Main Methods:

DaMiRseq utilizes an organized framework to preprocess and analyze RNA-Seq data.
The package incorporates methods for noise and bias removal.
Feature selection algorithms are employed to identify the most informative genomic features.

Main Results:

DaMiRseq facilitates efficient noise and bias reduction in transcriptome data.
The framework enables accurate classification of samples based on genomic features.
It supports effective biomarker discovery from high-dimensional RNA-Seq datasets.

Conclusions:

DaMiRseq offers a powerful and convenient solution for analyzing complex RNA-Seq data.
The package enhances the utility of RNA-Seq for biomarker discovery and classification.
It is a valuable tool for researchers working with high-throughput genomic data.