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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...

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Related Experiment Video

Updated: Mar 11, 2026

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

Measuring intratumor heterogeneity by network entropy using RNA-seq data.

Youngjune Park¹, Sangsoo Lim¹, Jin-Wu Nam^2,3

¹Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-742, Korea.

Scientific Reports

|November 25, 2016

Summary

This summary is machine-generated.

This study introduces transcriptome-based intratumor heterogeneity (tITH) using RNA sequencing and network analysis. tITH effectively measures tumor heterogeneity and improves survival prediction compared to existing methods.

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Area of Science:

Genomics
Bioinformatics
Systems Biology

Background:

Intratumor heterogeneity (ITH) is a critical factor in tumor progression, metastasis, and recurrence.
Understanding ITH is vital for developing effective clinical applications and treatment strategies.

Purpose of the Study:

To develop a novel method for quantifying ITH using RNA sequencing data.
To assess the effectiveness of transcriptome-based ITH (tITH) in characterizing tumor heterogeneity and predicting patient survival.

Main Methods:

Utilized RNA sequencing data from tumor samples.
Modeled gene-gene dependencies using a protein interaction network.
Quantified ITH using an entropy-based distance metric (nJSD) based on Jensen-Shannon Divergence (JSD).
Validated tITH using human cancer cell line data, single-cell sequencing data, and TCGA pan-cancer datasets.

Main Results:

The developed tITH method aligns with established genome-based ITH inference methods.
tITH demonstrated superior performance in survival analysis compared to existing methods.
Analysis of mouse clonal evolution data confirmed tITH's consistency with genetic heterogeneity.
Identified cell cycle-related pathways as significant contributors to increased network heterogeneity during clonal evolution.

Conclusions:

The proposed transcriptome-based ITH (tITH) provides a robust RNA-level characterization of tumor heterogeneity.
tITH offers a valuable tool for cancer research, potentially improving diagnostic and prognostic capabilities.