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Related Experiment Video

Updated: Jul 11, 2025

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Transcriptome software results show significant variation among different commercial pipelines.

Cung Nawl Thawng¹, Geoffrey Battle Smith²

¹Biology Department and Molecular Biology Program, New Mexico State University, Las Cruces, NM, USA.

|November 3, 2023

Summary

Choosing the right transcriptome analysis software is crucial for accurate results. DNAstar-D (Deseq2) provides a more conservative approach to analyzing gene expression, especially for subtle biological responses to low-level radiation.

Keywords:

Fold-changes Low radiation Model organisms Pipeline RNA-Seq Transcriptome software

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Area of Science:

Environmental Science
Genomics
Bioinformatics

Background:

Investigating biological responses to low-level and background radiation.
Previous study identified DNAstar-D as a conservative tool for differential gene expression analysis in E. coli and C. elegans.

Purpose of the Study:

To compare transcriptome responses to varying radiation dose rates using different software packages.
To evaluate the performance of DNAstar-D against other software (CLC, DNAstar-E, Azenta) in analyzing RNA-Seq data.

Main Methods:

Utilized RNA-Seq data from three model organisms: E. coli, C. elegans, and Aedes aegypti.
Analyzed transcriptome responses to natural radiation sources at varying dose rates.
Compared differential gene expression results across multiple software pipelines (DNAstar-D, CLC, DNAstar-E, Azenta).

Main Results:

DNAstar-D consistently yielded a more conservative number of differentially expressed genes (DEGs) and lower fold-changes compared to CLC, DNAstar-E, and Azenta.
The CLC pipeline showed a significantly exaggerated gene expression response in terms of DEG numbers and fold-changes across studies.
Applying a 30-read minimum cutoff criterion reduced the exaggerated responses observed in most software packages.

Conclusions:

The choice of transcriptome analysis software significantly impacts the interpretation of gene expression data.
DNAstar-D (Deseq2) offers a more conservative and realistic transcriptome expression pattern, suitable for studies investigating subtle biological responses.
Exaggerated gene expression findings from software like CLC may require careful consideration and validation, especially in low-dose radiation studies.