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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.
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Related Experiment Video

Updated: Apr 8, 2026

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction.

Wenqian Zhang¹, Ying Yu², Falk Hertwig^3,4

¹BGI-Shenzhen, Main Building, Bei Shan Industrial Zone, Yantian District, Shenzhen, Guangdong, 518083, China.

|June 26, 2015

Summary

RNA sequencing (RNA-seq) offers superior cancer transcriptome characterization compared to microarrays. However, both RNA-seq and microarray-based models show similar performance for predicting clinical endpoints in neuroblastoma.

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Last Updated: Apr 8, 2026

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Published on: November 11, 2014

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Area of Science:

Genomics
Cancer Research
Bioinformatics

Background:

Gene expression profiling is crucial for identifying cancer biomarkers.
RNA sequencing (RNA-seq) offers advanced transcriptome analysis beyond microarrays.
Neuroblastoma serves as a model for evaluating gene expression-based predictive models.

Purpose of the Study:

To systematically compare RNA-seq and microarray-based classifiers for clinical endpoint prediction.
To evaluate the performance of predictive models using neuroblastoma data.
To assess the impact of different factors on model accuracy.

Main Methods:

Generated gene expression profiles from 498 primary neuroblastomas using both RNA-seq and microarrays.
Developed 360 predictive models for six clinical endpoints using training and validation sets.
Analyzed factors influencing model performance, including technology platform and data analysis pipelines.

Main Results:

RNA-seq provided more detailed transcriptomic information than microarrays.
Prediction accuracy was primarily influenced by the clinical endpoint's nature.
Technological platforms (RNA-seq vs. microarrays) did not significantly impact model performance.

Conclusions:

RNA-seq excels at characterizing cancer transcriptomes.
RNA-seq and microarray-based models demonstrate comparable performance in clinical endpoint prediction.
Findings guide the development and implementation of gene expression-based predictive models in clinical practice.