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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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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

CanID: A Robust and Accurate RNA-seq Expression-based Diagnostic Classification Scheme for Pediatric Malignancies.

Daniel K Putnam¹, Alexander M Gout¹, Delaram Rahbarinia¹

¹St Jude Children's Research Hospital, Department of Computational Biology, Memphis, TN 38105, USA.

Genomics, Proteomics & Bioinformatics

|November 29, 2025

Summary

We developed CanID, a machine learning model using gene expression data, to accurately classify pediatric cancer subtypes. This tool aids in precise cancer diagnosis and treatment strategies.

Keywords:

Cancer classification Hematologic malignancy Machine learning RNA sequencing Solid tumor

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Area of Science:

Computational biology
Genomics
Machine learning in oncology

Background:

Accurate cancer subtype classification is crucial for personalized medicine.
Existing methods face challenges in pediatric cancer due to evolving standards and analytical limitations.

Purpose of the Study:

To develop a robust machine learning classification scheme for pediatric cancer subtypes using transcriptomic data.
To address the limitations of current classifiers in precision and scope for pediatric cancers.

Main Methods:

Developed CanID, a stacked ensemble machine learning model.
Utilized gene-level RNA sequencing count data as the sole input.
Trained on 3203 pediatric cancer samples across 13 solid tumor and 38 hematologic malignancy subtypes.

Main Results:

Achieved 99% accuracy for solid tumors and 92%-93% for hematologic malignancies on external datasets.
Demonstrated robustness against data collection variations, class imbalance, and potential mislabeling.
Successfully classified challenging subtypes often difficult for clinical histology.

Conclusions:

CanID offers a highly accurate and robust transcriptome-based approach for pediatric cancer diagnosis and stratification.
This method advances tumor diagnosis and supports clinically meaningful stratification.
The CanID tool is publicly available on GitHub for broader research application.