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Updated: Apr 2, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Practical RNA-Seq with Spike-Ins: A Bench-to-Bioinformatics Guide.

T Blake Horton¹, Kanjana Laosuntisuk^1,2, 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

This summary is machine-generated.

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.

Keywords:

DESeq2 Differential expression Normalization Nu*Delta normalization Plant RNA-Seq RNA-sequencing Synthetic RNA spike-ins Transcriptional abundance edgeR

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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.