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Updated: Jun 10, 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

A sequence-based approach to identify reference genes for gene expression analysis.

Raj Chari¹, Kim M Lonergan, Larissa A Pikor

¹Department of Integrative Oncology, British Columbia Cancer Agency Research Centre, Vancouver, BC, Canada. rchari@bccrc.ca

BMC Medical Genomics

|August 5, 2010

Summary

This summary is machine-generated.

Selecting appropriate reference genes is crucial for accurate gene expression analysis. This study identifies novel, stable reference genes for lung cancer using SAGE and qPCR, improving differential expression analysis.

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Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Accurate gene expression analysis relies on selecting suitable endogenous controls or reference genes.
Reference gene suitability can vary significantly across different cancer types and experimental contexts.
Traditional methods for identifying reference genes from microarray data have limitations, including the need for normalization and relative measurements.

Purpose of the Study:

To identify highly stable and specific reference genes for gene expression analysis in lung cancer.
To evaluate the performance of newly identified reference genes compared to conventional ones.
To assess the impact of reference gene selection on differential gene expression analysis.

Main Methods:

Serial Analysis of Gene Expression (SAGE) profiles of lung samples were analyzed to identify stably expressed genes.
Permutation tests were used to compare gene expression profiles between non-malignant and malignant lung samples.
Quantitative RT-PCR (qPCR) was employed to validate the constancy and specificity of candidate reference genes across different tissue types and sample cohorts.

Main Results:

Conventional reference genes like ACTB and GAPDH exhibited high variability in lung cancer samples.
Reference genes optimized for lung cancer showed poor performance in breast and brain cancer datasets.
Reference genes identified via SAGE demonstrated low variability in qPCR validation.
Normalization using the identified reference genes significantly improved the statistical significance of key lung cancer pathways in microarray data analysis.

Conclusions:

NDUFA1, RPL19, RAB5C, and RPS18 are identified as optimal reference genes for normalizing lung tissue expression data.
The developed approach is applicable to next-generation sequencing data for identifying context-specific reference genes.
This study provides a robust method for selecting reliable reference genes, enhancing the accuracy of gene expression studies.