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

Palmitoleic acid content and composition-based nutritional quality of commercial omega-7 oils and supplements.

Frontiers in nutrition·2026

Same author

Flexible MOF nanosheet based membranes for high-flux antifouling oil/water separation and reverse cross-flow cleaning.

Journal of hazardous materials·2026

Same author

Correction: Exploring the role of ferroptosis in pemphigus: identification of diagnostic markers and regulatory mechanisms.

Frontiers in medicine·2026

Same author

Early Detriment Analysis of First-Line Nivolumab plus Ipilimumab-Based Therapy in Patients with Metastatic Non-Small Cell Lung Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026

Same author

ZBP1-driven pyroptosis-associated alveolar macrophages exacerbate epithelial dysfunction in sepsis.

Cell death & disease·2026

Same author

Context-dependent roles of lncRNA JPX in human cancers.

Discover oncology·2026

Search research articles

Related Experiment Video

Updated: Jan 17, 2026

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data.

Ting Gong¹, Joseph D Szustakowski

¹Biomarker Development, Translational Medicine, Novartis Institutes for BioMedical Research, Cambridge, MA 02139, USA. tinggong@gmail.com

Bioinformatics (Oxford, England)

|February 23, 2013

Summary

DeconRNASeq is a new R package that accurately estimates cell type proportions in heterogeneous tissues using mRNA sequencing data. This tool provides a reliable method for analyzing complex biological samples.

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

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Related Experiment Videos

Last Updated: Jan 17, 2026

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

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

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Gene expression measurements in heterogeneous tissues are complicated by varying cell type proportions.
Accurate deconvolution is crucial for interpreting mRNA sequencing (mRNA-Seq) data from complex biological samples.

Purpose of the Study:

To introduce DeconRNASeq, an efficient R package for deconvoluting heterogeneous tissues using mRNA-Seq data.
To provide a quantitative and high-resolution tool for analyzing complex tissue samples.

Main Methods:

DeconRNASeq employs a globally optimized non-negative decomposition algorithm via quadratic programming.
The R package estimates mixing proportions of distinct tissue types within next-generation sequencing data.

Main Results:

DeconRNASeq demonstrated feasibility and validity across various mixing levels and data sources using in silico mixed mRNA-Seq data.
High correlation was observed between predicted cell proportions and actual tissue fractions in benchmark datasets.

Conclusions:

DeconRNASeq offers a rigorous and efficient solution for deconvoluting heterogeneous tissues from mRNA-Seq data.
The package's modular design facilitates integration into custom analytical pipelines for diverse high-throughput data.