Related Experiment Video
Updated: Aug 5, 2026
Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
Published on: August 4, 2016
Optimizing bioinformatic workflows to extract clinically usable gene expression data from targeted tumor RNA
Xiaokang Pan1, Ashley Patton1, Yi Seok Chang1
1James Molecular Laboratory at Polaris, The Ohio State University Wexner Medical Center, Columbus, OH 43240, United States.
Motivation:
Targeted RNA sequencing (RNA-seq) is widely used to detect gene fusions in tumors but clinical use of expression data from panels in fusion-negative cases has been limited. Differential gene expression (DGE) profiling from these panels has the potential to improve tumor classification.
Results:
To facilitate this application, we compared methods for sequence read counting, gene normalization, and supervised and unsupervised clustering methods to optimize them for smaller gene sets. We derived DGE data from ∼200-gene RNA-seq fusion panels. Among five tools for read counting, featureCounts was the most rapid and robust. For DGE with DESeq2, we compared five normalization strategies and showed the five most stably expressed genes over multiple sets provided optimal centralization. The outputs of the optimized pipeline were then assessed by a newly constructed targeted panel that added a limited number of genes assessing cell lineage and tumor grade. Finally, the optimized pipeline was evaluated using mean centroid and principal component analysis and pathway analysis and compared to outputs from full RNA-seq on a common set of challenging tumors. Comparable tumor clustering was observed with RNA-seq and the redesigned targeted gene panel.
Availability And Implementation:
The data analyzed during the current study are available from the corresponding author on reasonable request.
Insights
Targeted RNA sequencing panels can improve tumor classification using differential gene expression (DGE) analysis. Optimized methods for read counting and normalization enable robust DGE profiling from smaller gene sets.
Area of Science:
- Oncology
- Bioinformatics
- Genomics
Background:
- Targeted RNA sequencing (RNA-seq) is crucial for detecting gene fusions in tumors.
- Clinical application of expression data from fusion panels in fusion-negative cases remains limited.
- Differential gene expression (DGE) profiling offers potential for enhanced tumor classification.
Purpose of the Study:
- To optimize methods for DGE profiling using targeted RNA-seq panels.
- To evaluate the efficacy of optimized pipelines for tumor classification.
- To compare DGE data from targeted panels with full RNA-seq.
Main Methods:
- Compared read counting tools, selecting featureCounts for speed and robustness.
- Evaluated five normalization strategies for DESeq2, identifying stable gene sets for optimal normalization.
- Assessed optimized pipeline using new targeted panels and compared results with full RNA-seq.
Main Results:
- FeatureCounts demonstrated superior performance in read counting for targeted panels.
- Optimal normalization using stably expressed genes improved DGE analysis.
- The optimized pipeline achieved comparable tumor clustering results to full RNA-seq.
Conclusions:
- An optimized DGE pipeline enhances the clinical utility of targeted RNA-seq panels.
- This approach facilitates improved tumor classification, even in fusion-negative cases.
- The findings support the use of targeted panels for robust gene expression analysis in oncology.
