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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: Jun 11, 2025

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

scPanel: a tool for automatic identification of sparse gene panels for generalizable patient classification using

Yi Xie¹, Jianfei Yang², John F Ouyang¹

¹Programme in Cardiovascular and Metabolic Disorders, Centre for Computational Biology, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore.

Briefings in Bioinformatics

|October 1, 2024

Summary

scPanel identifies minimal gene panels for disease classification using single-cell RNA sequencing (scRNA-seq) data. This computational framework enables accurate patient stratification with fewer biomarkers, reducing clinical translation costs.

Keywords:

clinical biomarkers deep learning machine learning marker panel patient classification single-cell RNA-seq

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Last Updated: Jun 11, 2025

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Area of Science:

Computational biology
Genomics
Biomarker discovery

Background:

Single-cell RNA sequencing (scRNA-seq) enables transcriptomic profiling at single-cell resolution in large patient cohorts.
Discovery of gene and cellular biomarkers is crucial for disease understanding and patient stratification.
High costs associated with large gene panels hinder clinical translation of scRNA-seq findings.

Purpose of the Study:

To introduce scPanel, a computational framework for identifying sparse gene panels for patient classification.
To bridge the gap between biomarker discovery and clinical application by reducing gene panel size.
To enable accurate patient classification using minimal, automatically selected informative biomarker genes.

Main Methods:

scPanel identifies informative biomarker genes from cell populations most responsive to perturbations.
A data-driven approach automatically determines a minimal set of biomarker genes.
Patient-level classification is achieved by aggregating cell-associated prediction probabilities using the area under the curve score.

Main Results:

scPanel achieved high patient classification accuracy in scleroderma, colorectal cancer, and COVID-19 datasets using <20 genes.
The framework demonstrated cross-dataset generalizability for predicting disease state in an external COVID-19 cohort.
scPanel outperformed existing gene selection methods for patient classification.

Conclusions:

scPanel effectively identifies parsimonious sets of reliable biomarker candidates for clinical translation.
The framework reduces the cost and complexity of translating scRNA-seq discoveries into clinical applications.
scPanel facilitates the development of cost-effective diagnostic and prognostic tools based on transcriptomic data.