Practical RNA-Seq with Spike-Ins: A Bench-to-Bioinformatics Guide
T Blake Horton1, Kanjana Laosuntisuk1,2, Colleen J Doherty3
1Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC, USA.
Methods in Molecular Biology (Clifton, N.J.)
|April 1, 2026
Summary
Exogenous RNA spike-ins improve RNA-Sequencing (RNA-Seq) analysis in plants by normalizing gene expression data. This method enhances the accuracy of identifying differentially expressed genes (DEGs) for reliable biological conclusions.
Area of Science:
- Plant biology
- Molecular biology
- Bioinformatics
Background:
- RNA-Sequencing (RNA-Seq) is crucial for plant gene expression analysis.
- Normalization is essential for accurate RNA-Seq data comparison.
- Traditional normalization methods assume stable transcript levels, often violated in plant studies, leading to inaccurate differentially expressed gene (DEG) identification.
Purpose of the Study:
- To present protocols for integrating exogenous RNA spike-ins in plant RNA-Seq experiments.
- To improve the accuracy, specificity, and sensitivity of DEG calling.
- To lower adoption barriers for spike-in-based normalization in plant transcriptional analysis.
Main Methods:
- Bench protocols for exogenous RNA spike-in integration.
- Computational analysis protocols for spike-in data.
- Calculation of spike-in amounts and implementation of spike-in-based normalization strategies.
- Comparison of spike-in normalization with distribution-based methods.
Main Results:
- Exogenous RNA spike-ins provide a robust solution for controlling variations in total mRNA levels and technical biases.
- Spike-in normalization strategies improve the accuracy of DEG calling compared to traditional methods.
- Protocols facilitate the integration of spike-ins for more reliable plant transcriptional analysis.
Conclusions:
- Adopting exogenous RNA spike-ins enhances the reliability and interpretability of plant RNA-Seq results.
- Spike-in normalization is recommended for plant studies where transcript abundance may vary.
- This approach leads to more accurate identification of biologically relevant gene expression changes.
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