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Updated: Mar 9, 2026

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Identifying stably expressed genes from multiple RNA-Seq data sets.

Bin Zhuo¹, Sarah Emerson¹, Jeff H Chang²

¹Department of Statistics, Oregon State University , Corvallis , OR , United States.

|December 29, 2016

Summary

This summary is machine-generated.

This study identifies stably expressed genes in Arabidopsis RNA-Seq data, crucial for accurate gene expression analysis. Understanding RNA-Seq count variation helps improve data normalization and differential expression conclusions.

Keywords:

Numerical stability measure RNA-Seq Reference gene set Stably expressed gene

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Area of Science:

Plant biology
Genomics
Bioinformatics

Background:

RNA-Seq is a powerful tool for gene expression analysis.
Identifying stably expressed genes is critical for accurate data interpretation.
Variation in RNA-Seq data can arise from multiple sources.

Purpose of the Study:

To identify stably expressed genes across diverse Arabidopsis RNA-Seq experiments.
To analyze sources of variation in RNA-Seq count data.
To provide recommendations for improving RNA-Seq data analysis.

Main Methods:

Examined RNA-Seq data from 211 Arabidopsis biological samples across 24 experiments.
Grouped samples by tissue type and identified stably expressed genes.
Applied Poisson log-linear mixed-effect models to decompose variance components.

Main Results:

Identified genes with stable expression across samples, treatments, and experiments.
Quantified variance components, including between-sample, between-treatment, and between-experiment variations.
Demonstrated that the choice of normalization and reference gene sets impacts stability measures.

Conclusions:

Stably expressed genes are essential for robust RNA-Seq normalization and differential expression analysis.
Differential expression is a relative concept influenced by normalization strategies.
Recommends using explicit and common reference gene sets for improved interpretability and consistency in multi-experiment RNA-Seq analyses.